Hot spots in a wireless sensor network emerge as locations under heavy traffic load. Nodes in such areas quickly deplete energy resources, leading to disruption in network services. This problem is common for data collection scenarios in which Cluster Heads (CH) have a heavy burden of gathering and relaying information. The relay load on CHs especially intensifies as the distance to the sink decreases. To balance the traffic load and the energy consumption in the network, the CH role should be rotated among all nodes and the cluster sizes should be carefully determined at different parts of the network. This paper proposes a distributed clustering algorithm, Energy-efficient Clustering (EC), that determines suitable cluster sizes depending on the hop distance to the data sink, while achieving approximate equalization of node lifetimes and reduced energy consumption levels. We additionally propose a simple energy-efficient multihop data collection protocol to evaluate the effectiveness of EC and calculate the end-to-end energy consumption of this protocol; yet EC is suitable for any data collection protocol that focuses on energy conservation. Performance results demonstrate that EC extends network lifetime and achieves energy equalization more effectively than two well-known clustering algorithms, HEED and UCR.

An Energy-Efficient Clustering Solution for Wireless Sensor Networks

WSNs (Wireless Sensor Networks) can collect reliable and accurate information in distant and hazardous environments, and can be used in National Defence, Military Affairs, Industrial Control, Environmental Monitor, Traffic Management, Medical Care, Smart Home, etc. The sensor whose resources are limited is cheap, and depends on battery to supply electricity, so it’s important for routing to efficiently utilize its power. In this paper, an energy-efficient Single-Hop Active Clustering (SHAC) algorithm is proposed for wireless sensor networks. The core of SHAC has three parts. Firstly, a timer mechanism is introduced to select tentative cluster-heads. By analyzing relation between time of timer and residual energy, it is known that time of timer is inversely proportional to residual energy of nodes so a timer mechanism can balance the residual energy of the whole network nodes which improves the network energy efficiency. Secondly, a cost function is proposed to balance energy-efficient of each node. Finally, an active clustering algorithm is proposed for single-hop homogeneous networks. Through both theoretical analysis and numerical results, it is shown that SHAC prolongs the network lifetime significantly against the other clustering protocols such as LEACH-C and EECS. Under general instance, SHAC may prolong the lifetime by up to 50% against EECS.

An Energy-Efficient Communication Protocol for Wireless Sensor Networks

A suitable clustering algorithm for grouping sensor nodes can increase the energy efficiency of WSNs. However, clustering requires additional overhead, such as cluster head selection and assignment, and cluster construction. This paper proposes a new regional energy aware clustering method using isolated nodes for WSNs, called Regional Energy Aware Clustering with Isolated Nodes (REAC-IN). In REAC-IN, CHs are selected based on weight. Weight is determined according to the residual energy of each sensor and the regional average energy of all sensors in each cluster. Improperly designed distributed clustering algorithms can cause nodes to become isolated from CHs. Such isolated nodes communicate with the sink by consuming excess amount of energy. To prolong network lifetime, the regional average energy and the distance between sensors and the sink are used to determine whether the isolated node sends its data to a CH node in the previous round or to the sink. The simulation results of the current study revealed that REAC-IN outperforms other clustering algorithms.

Energy Efficient Clustering Scheme for Prolonging the Lifetime of Wireless Sensor Network With Isolated Nodes

Typically, a wireless sensor network contains an important number of inexpensive power constrained sensors, which collect data from the environment and transmit them towards the base station in a cooperative way. Saving energy and therefore, extending the wireless sensor networks lifetime, imposes a great challenge. Clustering techniques are largely used for these purposes. In this paper, we propose and evaluate a clustering technique called a Developed Distributed Energy-Efficient Clustering scheme for heterogeneous wireless sensor networks. This technique is based on changing dynamically and with more efficiency the cluster head election probability. Simulation results show that our protocol performs better than the Stable Election Protocol (SEP) by about 30% and than the Distributed Energy-Efficient Clustering (DEEC) by about 15% in terms of network lifetime and first node dies.

Developed Distributed Energy-Efficient Clustering (DDEEC) for heterogeneous wireless sensor networks

Wireless sensor networks are appearing as an emerging need for mankind. Though, Such networks are still in research phase however, they have high potential to be applied in almost every field of life. Lots of research is done and a lot more is awaiting to be standardized. In this work, cluster based routing in wireless sensor networks is studied precisely. Further, we modify one of the most prominent wireless sensor network's routing protocol "LEACH" as modified LEACH (MODLEACH) by introducing efficient cluster head replacement scheme and dual transmitting power levels. Our modified LEACH, in comparison with LEACH out performs it using metrics of cluster head formation, through put and network life. Afterwards, hard and soft thresholds are implemented on modified LEACH (MODLEACH) that boast the performance even more. Finally a brief performance analysis of LEACH, Modified LEACH (MODLEACH), MODLEACH with hard threshold (MODLEACHHT) and MODLEACH with soft threshold (MODLEACHST) is undertaken considering metrics of throughput, network life and cluster head replacements.

MODLEACH: A Variant of LEACH for WSNs

Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks

PI is a newly proposed Active Queue Management algorithm that has many important applications. But in case of heavy congestion its response is sluggish, and because of its static parameter's setting PI is sensitive to network status, such as RTT, capacity of neck-link, and number of TCP flows. To overcome these shortcomings of PI algorithm, we propose a novel AQM scheme, called Neuron based PI or NPI. NPI takes PI controller as an ADALINE with two inputs, and the proportional and integral factors of the controller are adjusted online by LMS algorithm. Simulation results show that under NPI the queue length converges to the desired value quickly and the oscillation is small.

Designing adaptive PI algorithm based on single neuron

For scale-model measurement of the RCS of a lossy target, it is difficult to meet all the requirements of physical similarity [1], especially in the constitutive parameters of the absorbent materials because of their frequency dependence. One often avoids this problem by keeping the wavelength of incident radiation fixed with the sizes of the target scaled down. In this case, the scale factor to evaluate the RCS of prototype from that of a scale model must be found. This article gives brief expressions of the RCS and the scale factors for simply shaped scatterers by means of a PO approximation and the method of stationary phase. It is found that the physical scale factor q2 is not always proportional to the geometrical scale factor p2, but to p to the powers of 2-4 for a variety of shaped scatterers, respectively, provided their characteristic dimensions are much greater than the working wavelength. © 1993 John Wiley & sons, Inc.

Effects of variance in the size of the scatterer on its radar cross section

\n\nOrganic conjugated materials have shown attractive applications due to their good optoelectronic\nproperties, which enable them solution processing techniques in organic optoelectronic devices.\nMany conjugated materials have been investigated in polymer solar cells and organic field-effect transistors.\nAmong those conjugated materials, Benzo[1,2-b:4,5-b′]dithiophene (BDT) is one of the most\nemployed fused-ring building groups for the synthesis of conjugated materials. The symmetric and planar\nconjugated structure, tight and regular stacking of BDT can be expected to exhibit the excellent carrier\ntransfer for optoelectronics. In this review, we summarize the recent progress of BDT-based conjugated\npolymers in optoelectronic devices. BDT-based conjugated materials are classified into onedimensional\n(1D) and two-dimensional (2D) BDT-based conjugated polymers. Firstly, we introduce the\nfundamental information of BDT-based conjugated materials and their application in optoelectronic devices.\nSecondly, the design and synthesis of alkyl, alkoxy and aryl-substituted BDT-based conjugated\npolymers are discussed, which enables the construction of one-dimensional and two-dimensional BDTbased\nconjugated system. In the third part, the structure modification, energy level tuning and morphology\ncontrol and their influences on optoelectronic properties are discussed in detail to reveal the structure-\nproperty relationship. Overall, we hope this review can be a good reference for the molecular design\nof BDT-based semiconductor materials in optoelectronic devices.\n

Optoelectronic Properties and Structural Modification of Conjugated Polymers Based on Benzodithiophene Groups

To improve the computational performance of the fuzzy C-means (FCM) algorithm used in dataset clustering with large numbers, the concepts of the equivalent samples and the weighting samples based on eigenvalue distribution of the samples in the feature space were introduced and a novel fast cluster algorithm named weighted fuzzy C-means (WFCM) algorithm was put forward, which came from the traditional FCM algorithm. It was proved that the cluster results were equivalent in dataset with two different cluster algorithms: WFCM and FCM. Furthermore, the WFCM algorithm had better computational performance than the ordinary FCM algorithm. The experiment of the gray image segmentation showed that the WFCM algorithm is a fast and effective cluster algorithm.

Li Qing

Papers

Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks

Designing adaptive PI algorithm based on single neuron

Effects of variance in the size of the scatterer on its radar cross section

Optoelectronic Properties and Structural Modification of Conjugated Polymers Based on Benzodithiophene Groups

A research on fast FCM algorithm based on weighted sample