Clustered Sensor Networks Research Articles

Decentralized Detection in Clustered Sensor Networks

We investigate decentralized detection in clustered sensor networks with hierarchical multi-level fusion. We focus on simple majority-like fusion strategies, leading to closed-form analytical performance evaluation. The sensor nodes observe a binary phenomenon and transmit their own data to an access point (AP), possibly through intermediate fusion centers (FCs). We investigate the impact of uniform and nonuniform clustering on the system performance, evaluated in terms of probability of decision error on the phenomenon status at the AP. Our results show that, under a majority-like fusion rule, uniform clustering leads to the minimum performance degradation, which depends only on the number of decision levels rather than on the specific clustered topology. We then extend our approach, taking into account the impact of spatial variations of the phenomenon, noisy communication links, and weighed fusion rules. Finally the proposed distributed detection schemes are characterized with simulation and experimental results (relative to IEEE 802.15.4-based networks), which confirm the analytical predictions.

H-cluster: A Novel Efficient Algorithm for Data Clustering in Sensor Networks

This paper focuses on the problem of data clustering in wireless sensor networks (WSNs). The data time window is a landmark window, from the time WSN starts working up to the current time. The objective is to group sensory data generated by sensor nodes deployed in a two-dimensional physical space by the similarity of sensory data in the multi-dimensional sensory data space. To perform in-network data clustering efficiently, we propose HilbertMap, a novel dimensionality reduction technique based on the Hilbert Curves, to map a multi-dimensional data space to a two-dimensional physical space. Through this mapping, the communications for clustering mostly occur between geographically nearby sensor nodes. We have conducted simulation experiments on both real-world and synthetic datasets. Our results show that HilbertMap improves the communication efficiency while maintaining a good clustering quality.

멀티홉 클러스터 센서 네트워크 환경 기반에서 견고한 키 교환

본 논문에서는 신뢰하는 제 3기관(혹은 장치)(TTP)이 없고 사전에 키가 분배되지 않는 멀티 홉 클러스터 센서 네트워크 환경에서 안전하게 암호화된 키를 교환하는 알고리즘을 제안한다. 기존의 연구는 TTP가 존재하거나 노드간 키가 이미 분배되었다는 가정하에서 진행되었다. 그러나, 기존의 방법들은 기반 구조가 없는 USN 환경에서는 가능하지 않다. 기존 연구 중 일부는 난수를 이용한 Diffie-Hellman 알고리즘을 이용하여 문제를 해결하고자 하였으나 재생 공격과 중간자 공격에 취약한 것으로 나타났다. 기존의 Diffie-Hellman 알고리즘에서 취약한 문제로 드러난 노드 간 인증 문제는 ��TESLA를 사용한 수정된 Diffie-Hellman 알고리즘으로 해결한다. 본 논문에서는 수정된 Diffie-Hellman 알고리즘에 타임 스탬프를 사용한 일회용 패스워드(OTP)를 추가하여 안전하면서, 가볍고, 강인한 키 교환 알고리즘을 제안한다. 마지막으로 인증, 기밀성, 무결성, 부인방지, 후방향 안전성 및 전방향 안전성에 대해서 안전하다는 것을 검증한다. In this paper, we proposed a scheme that it safely exchanges encrypted keys without Trust Third Party (TTP) and Pre-distributing keys in multi-hop clustering sensor networks. Existing research assume that it exists a TTP or already it was pre-distributed a encrypted key between nodes. However, existing methods are not sufficient for USN environment without infrastructure. Some existing studies using a random number Diffie-Hellman algorithm to solve the problem. but the method was vulnerable to Replay and Man-in-the-middle attack from the malicious nodes. Therefore, authentication problem between nodes is solved by adding a ��TESLA. In this paper, we propose a modified Diffie-Hellman algorithm that it is safe, lightweight, and robust pair-wise agreement algorithm by adding One Time Password (OTP) with timestamp. Lastly, authentication, confidentiality, integrity, non-impersonation, backward secrecy, and forward secrecy to verify that it is safe.

A study of subdividing hexagon-clustered WSN for power saving: Analysis and simulation

Hexagons are an ideal shape for clustering sensor networks, for it can seamlessly divide clustered areas, and is the largest regular polygon (in terms of the number of sides) that has this property. In this paper, we analyze the benefit of subdividing a hexagonal cluster for the purpose of reducing the overall power consumption in the cluster. Assuming a spatial Poisson distribution of sensor nodes in the cluster, we propose a subdivision scheme, and perform a comprehensive analytical estimate of power savings brought about by the subdivision. The analytical results show that subdivision will yield considerable saving in overall power consumption of the cluster, and the saving is heavily dependent on the nodes’ transmission range and their deployment density. The limit on the depth of subdivision is also analyzed. Simulation results are presented, and their implication discussed.

PERSEN: power-efficient logical ring based key management for clustered sensor networks

Cluster-based organisation is widely used to achieve energy efficiency in Wireless Sensor Networks (WSNs). To achieve confidentiality of the sensed data, it is necessary to have a secret key shared between a node and its Cluster Head (CH). Key management is challenging as the CH changes in every round in cluster-based organisation. In this paper, we propose two schemes for key management in clustered sensor networks with changing CH. In Simple Secure Logical Ring (SSLR) scheme, communication and computation cost incurred for key establishment is constant. In Burmester Desmedt Logical Ring (BDLR) scheme, key establishment is achieved by performing (n + 1) multiplications and (n + 5) communications, where n is the number of nodes in the cluster. Simulation results show that node compromise in the proposed schemes is handled efficiently.

A Novel Biased Energy Distribution (BED) Technique for Cluster-Based Routing in Wireless Sensor Networks

Abstract This paper presents the impact of utilizing a biased energy distribution (BED) scheme for clustering sensor networks. In clustering sensor networks, some of the nodes are elected as aggregators and they compress the data from their cluster members before sending the aggregated data to the sink. Existing clustering routing protocols assume that all the nodes are provided with equal amount of energy but this shortens the network lifetime and makes the network unstable. This paper proposes a solution prioritizing the network into higher and lower energy nodes. The aim of this approach is to ensure well balanced energy consumption in order to maximize network lifetime. It is shown by simulation that the proposed technique exhibits better performance when compared to existing clustering routing techniques in terms of throughput, network lifetime and energy consumption.

Energy-efficient clustering for Wireless Sensor Networks-A Survey

Research in clustering has been very active in the last two decades. Clustering schemes decreases the communication overheads, intern reducing the power consumptions and interferences among nodes. Clustering is one of the best known methods used to face this type of challenges. In this paper, we study and compare various objectives, features & characteristics of some of the popularly explored clustering algorithms & published clustering schemes in Wireless Sensor Networks like advantages and disadvantages of respective clustering schemes. This paper also presents future research topics and highlights the challenges in clustering in Wireless sensor networks. Keywords: Wireless Sensor Networks, Cluster Head, Cluster-based routing, Hierarchical clustering, Base Station

Stochastic and Equitable Distributed Energy-Efficient Clustering (SEDEEC) for heterogeneous wireless sensor networks

The hierarchical clustering technique reduces strongly direct transmissions which consumes hardly the nodes energy. Many new protocols are specifically designed for wireless sensor networks using this strategy, where the objectives are to save energy and extend the network's lifetime. Adapting this approach, we propose a more equitable and stochastic technique which distributes uniformly the energy consumed trough the whole network using a dynamic probability to elect the Cluster Head (CH). This protocol is used for more energy efficiency distribution where the BS is localised far away from the network. Moreover, it is used where the collect data defines the maximum or the minimum values in supervised region. Simulation results show that our protocol enlarges the network lifetime and performances results compared to the Low-Energy Adaptive Clustering Hierarchy (LEACH), the Distributed Energy-Efficient Clustering (DEEC) and the Equitable Distributed Energy-Efficient Clustering (EDEEC).

Heterogeneous Clustering Of Sensor Network

In this paper, we proposed a heterogeneous clustering of sensor network in which different types of sensors exist that report to different sinks. This heterogeneous clustering approach has its own security measures in which only registered devices in the IP-DNS can be part of the heterogeneous clustering network and can set up communication channel with the sink or with any other device. We have also proposed a dynamic hierarchical clustering scheme in which certain number of sensor nodes that locates in specific area will be grouped as cluster and nominate a cluster head of their own. We have proposed two cluster head selection criteria. The fist one is edge sensors in which sensors that are closer to other clusters will be selected as cluster head whereas the second select the cluster head that has the minimum average hop distance to other sensors. We have seen that the second selection criteria leads to shorter path as the cluster heads are in the middle of cluster and closer to other nodes. We have compared our proposed architecture with other well known on demand routing protocol named as AODV and we have seen that our approach perform way better in terms of delay. Our approach can allow the usage of proactive protocols at each routing level and still have limited size of routing table at each node. The only trade off is that our approach has slightly longer path length compared to AODV. Furthermore, we proposed a hierarchical routing scheme in which sensor networks at each routing level is grouped and exchange routing information among themselves avoiding flooding the network with control over head messages.

Multiple Parameter Based Clustering (MPC): Prospective Analysis for Effective Clustering in Wireless Sensor Network (WSN) Using K-Means Algorithm

In wireless sensor network cluster architecture is useful because of its inherent suitability for data fusion. In this paper we represent a new approach called Multiple Parameter based Clustering (MPC) embedded with the traditional k-means algorithm which takes different parameters (Node energy level, Euclidian distance from the base station, RSSI, Latency of data to reach base station) into consideration to form clusters. Then the effectiveness of the clusters is evaluated based on the uniformity of the node distribution, Node range per cluster, Intra and Inter cluster distance and required energy level of each centroid. Our result shows that by varying multiple parameters we can create clusters with more uniformly distributed nodes, minimize intra and maximize inter cluster distance and elect less power consuming centroid.

In-Band Spectrum Sensing in IEEE 802.22 WRANs for Incumbent Protection

In cognitive radios, in-band spectrum sensing is essential for the protection of legacy spectrum users, enabling secondary users to vacate channels immediately upon detection of primary users. For in-band sensing, it is important to meet detectability requirements, such as the maximum allowed detection latency and the probability of misdetection and false alarm. In this paper, we propose key techniques for efficient in-band sensing. We first advocate the use of clustered sensor networks, and propose a periodic in-band sensing algorithm that optimizes sensing period and sensing time to meet the detectability requirements while minimizing sensing overhead. The scheme also determines the better of energy or feature detection incurring less sensing overhead at each SNR level, and derives the threshold aRSS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">threshold</sub> on the average received signal strength of a primary signal above which energy detection is preferred to feature detection. We consider two key factors affecting aRSS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">threshold</sub> noise uncertainty and inter-CRN interference. aRSS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">threshold</sub> appears to lie between -114.6 dBm and -109.9 dBm with noise uncertainty ranging from 0.5 dB to 2 dB, and between -112.9 dBm and -110.5 dBm with 1-6 interfering CRNs. We also investigate how strict the detection requirement must be for efficient reuse of idle channels without incurring unnecessary channel switches due to false detection of primaries.

RSSI-based gradient clustering for wireless sensor networks

Cluster-based structure is generally used for Wireless Sensor Network ( WSN) in order to reduce the overhead of transmissions and energy consumption. Concerning the problem of load unbalance of nodes in consideration for the mode of data collection in WSN, a RSSI-based Gradient Clustering ( RGC) algorithm was proposed. RGC built the gradient information based on nodes' RSSI values, and then the hierarchical structure of the networks was formed. Taking cluster heads with same gradients or not into consideration, the load-balancing RGC was designed, which reduced the complexity of clustering algorithms and energy consumption efficiently, and the networks lifetime was also lengthened. The simulation results verify the correctness and efficiency of RGC.

Secure Clustering and Symmetric Key Establishment in Heterogeneous Wireless Sensor Networks

Information security in infrastructureless wireless sensor networks (WSNs) is one of the most important research challenges. In these networks, sensor nodes are typically sprinkled liberally in the field in order to monitor, gather, disseminate, and provide the sensed data to the command node. Various studies have focused on key establishment schemes in homogeneous WSNs. However, recent research has shown that achieving survivability in WSNs requires a hierarchy and heterogeneous infrastructure. In this paper, to address security issues in the heterogeneous WSNs, we propose a secure clustering scheme along with a deterministic pairwise key management scheme based on public key cryptography. The proposed security mechanism guarantees that any two sensor nodes located in the same cluster and routing path can directly establish a pairwise key without disclosing any information to other nodes. Through security performance evaluation, it is shown that the proposed scheme guarantees node-to-node authentication, high resiliency against node capture, and minimum memory space requirement.

Distributed Unsupervised Gaussian Mixture Learning for Density Estimation in Sensor Networks

This paper considers the problem of density estimation and clustering in sensor networks. It is assumed that measurements of the sensors can statistically be modeled by a common Gaussian mixture model (GMM). In this paper, a distributed expectation maximization (DEM) algorithm is developed to estimate the model order and the parameters of this model. Scalability and fault tolerance are two important advantages of this method. In the E-step of this algorithm, each node calculates local sufficient statistics using its local observations. A distributed averaging approach is then used to diffuse local sufficient statistics to neighboring nodes and estimate global sufficient statistics in each node. In the M-step, each node updates parameters of the GMM using the estimated global sufficient statistics. Diffusion speed and convergence of the proposed algorithm are also studied. The proposed method is then used for environmental monitoring and also distributed target classification. Simulation results approve the promising performance of this algorithm.

Formal modeling and analysis approach of wireless sensor network

Petri net,as a formal tool used in the research of clustering and node coverage of wireless sensor network,can help to describe the wireless sensor network formally and establish the corresponding formal model quickly.As the Petri net has solid mathematical theory,it can better research the energy constraint problem of wireless sensor network in the process of node clustering and covering.The formal model can provide the theoretical and numerical basis for the design of a better clustering and covering structure,and then help to promote the existing algorithm.

New clustering protocol to decrease probability failure nodes and increasing the lifetime in WSNs

Clustering in wireless sensor networks is one of the crucial methods for increasing of network lifetime. There are many algorithms in for clustering. LEACH algorithm is one of the important clusters based in wireless sensor network. In this paper we proposed a new clustering method for increasing of network lifetime. In this we distribute several sensors with a high-energy for managing the cluster heads and decrease their responsibilities in network. These sensors are used as a gateway between head cluster and base station. The simulation results shows, new clustering protocol decrease probability of failure nodes and increasing lifetime in WSNs.

A simple information-theoretic analysis of clustered sensor networks with decentralized detection

In this letter, we present a simple information-theoretic framework to analyze clustered sensor networks with hierarchical multi-level majority-like fusion and decentralized detection. The sensor nodes observe a binary phenomenon and transmit their own data to an access point (AP), possibly through intermediate fusion centers (FCs). We investigate the impact of uniform and non-uniform clustering on the system performance, evaluated in terms of mutual information between the true phenomenon status and its estimate at the AP. Being the overall system binary-input binary-output (BIBO), it will be shown that the probability of decision error (Pe) is a specific function of the input-output mutual information (I). In other words, the network operational point lies over a specific Pe - I curve and depends on the network characteristics (e.g., topology, observation and communication noise levels, etc.).

A Multipath Routing Protocol Based on Clustering and Ant Colony Optimization for Wireless Sensor Networks

For monitoring burst events in a kind of reactive wireless sensor networks (WSNs), a multipath routing protocol (MRP) based on dynamic clustering and ant colony optimization (ACO) is proposed. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is their limited power supply, and therefore some metrics (such as energy consumption of communication among nodes, residual energy, path length) were considered as very important criteria while designing routing in the MRP. Firstly, a cluster head (CH) is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in the search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance of energy consumption among nodes and reduce the average energy consumption effectively.

근사 최소 스타이너 트리를 이용한 효율적인 클러스터 센서 네트워크의 구성

Cluster sensor network is a sensor network where input nodes crowd densely around some nuclei. Steiner minimum tree is a tree connecting all input nodes with introducing some additional nodes called Steiner points. This paper proposes a mechanism for efficient construction of a cluster sensor network connecting all sensor nodes and base stations using connections between nodes in each belonged cluster and between every cluster, and using repetitive constructions of approximate Steiner minimum trees. In experiments, while taking 1170.5% percentages more time to build cluster sensor network than the method of Euclidian minimum spanning tree, the proposed mechanism whose time complexity is O() could spend only 20.3 percentages more time for building 0.1% added length network in comparison with the method of Euclidian minimum spanning tree. The mechanism could curtail the built trees` average length by maximum 3.7 percentages and by average 1.9 percentages, compared with the average length of trees built by Euclidian minimum spanning tree method.

A game theoretical approach to clustering of ad-hoc and sensor networks

Game theory has been used for decades in fields of science such as economics and biology, but recently it was used to model routing and packet forwarding in wireless ad-hoc and sensor networks. However, the clustering problem, related to self-organization of nodes into large groups, has not been studied under this framework. In this work our objective is to provide a game theoretical modeling of clustering for ad-hoc and sensor networks. The analysis is based on a non-cooperative game approach where each sensor behaves selfishly in order to conserve its energy and thus maximize its lifespan. We prove the Nash Equilibria of the game for pure and mixed strategies, the expected payoffs and the price of anarchy corresponding to these equilibria. Then, we use this analysis to formulate a clustering mechanism (which we called Clustered Routing for Selfish Sensors--CROSS), that can be applied to sensor networks in practice. Comparing this mechanism to a popular clustering technique, we show via simulations that CROSS achieves a performance similar to that of a very popular clustering algorithm.