Perceptually Important Points-Based Data Aggregation Method for Wireless Sensor Networks

The wireless sensor network (WSN) has been among the fast-growing areas in recent years in different fields, like health applications, military applications, the operations of disaster relief, etc. In an unattended, even hostile environment, nodes in WSNs are commonly deployed. What is worse, these nodes are fitted with minimal resources for communication, computation, storage and battery. It is also impossible to guarantee the lifespan of WSNs without reducing their network performance. A massive volume of application-specific data is produced by WSNs. Such data must be processed and sent to the base station by sensor nodes, which is an expensive matter. As the resources of sensor nodes are constrained, the key challenges facing WSNs are effective data processing and energy conservation. To tackle those challenges, data aggregation can be used, which is a popular approach that filters redundant data in-network and speeds up the extraction of knowledge. In this paper, we propose an energy-saving data aggregation method for WSNs based on the extraction of extrema points (DAEP) to greatly minimize the energy consumption of each node and boost the higher lifespan of WSN. The efficiency of the proposed method was measured by performing multiple simulation experiments based on real sensor readings collected in Intel Berkeley laboratories and comparing the results achieved with some work in the literature. The results show the ability of the proposed method to reduce the load on the sensor nodes in terms of reducing the amount of transmitted data by up to 69-80%, energy consumed by 73-77% while maintaining an acceptable level of accuracy compared to some existing works.

A three-dimensional (3D) orthogonal frequency division multiplexing (OFDM) system with peak-to-average power ratio (PAPR) reduction method for the wireless sensor networks (WSNs) is presented. The transmit power of wireless nodes in an ad hoc network is strictly limited. Thus, high PAPR of the 3D OFDM system is a possible drawback when it is considered as a physical layer transmission scheme for the WSNs. Here, we propose an improved partial transmit sequence (PTS) technique to reduce PAPR of the OFDM system with 3D modulation formats. In the proposed algorithm, the components of 3D signals are assigned to different subblocks using a diagonal rule (DR), increasing randomness of the signals in the disjoint subblocks forcibly. As a result, the proposed method reduces PAPR of the 3D OFDM system significantly without increase in computational complexity. Hence, the proposed algorithm makes the 3D OFDM system be a possible candidate for a physical layer transmission scheme in future WSNs.

Node localization is a fundamental research problem in wireless sensor networks, and the implementation of many applications requires the accurate location of sensor nodes. Currently, among the existing localization algorithms, non-ranging algorithms are a class of algorithms that localize sensor nodes through multi-hop connectivity information between them, and the DV_Hop algorithm is one of the most frequently used algorithms. Although the implementation of DV_Hop algorithm is relatively simple, its location accuracy is low, which is difficult to meet the higher requirements of the application. For this reason, this paper proposes an improved non-ranging node localization method for wireless sensor networks. The proposed algorithm adopts the minimum mean square error criterion to calculate the average hop distance of anchor nodes. On this basis, the distance estimation from unknown node to anchor node is replaced. Finally, a nonlinear iterative approach is used to perform the multilateral localization process. The experimental results show that the localization accuracy and stability of the proposed algorithm are also significantly improved.

In this paper, we propose an energy-efficient data query method for wireless sensor networks (WSNs). Our method is flexible and adaptive. To solve the cluster head coverage problem in our method, we propose virtual grid clustering (VGC) protocol in which the clusters are constructed only in the query area when they are needed. The clusters are destroyed after the query ends. Theoretical analysis and simulations show that our method can save significant amount of energy comparing to routing tree protocol and LEACH.

In recent years, with the development of the Internet of things industry, wireless sensor networks, as one of the most important technologies in its sensing layer, have been further studied and applied. However, the security threats it faces are becoming more and more diverse, and the security needs are becoming more and more urgent. However, wireless sensor network nodes are mutually exclusive, which leads to low accuracy of attack path prediction when malicious attacks occur. In order to ensure the security of wireless sensor networks, a hidden Markov based malicious attack path prediction method for wireless sensor networks is designed. The method of authentication coding is introduced to realize data integrity authentication. Extract the malicious software gene sequence, define the constraint conditions of the state transition probability matrix, build the attack means deduction model based on Hidden Markov, and realize the malicious attack path prediction. Experimental results: the average accuracy of the proposed method in four malicious attack scenarios is 73.583%, 55.674%, 32.923% and 85.014%, respectively. The results also verify that the proposed method improves the accuracy of malicious attack path prediction in wireless sensor networks based on Hidden Markov model and has good feasibility. It provides an important basis for wireless sensor network administrators to implement defense strategies in time.

An energy-efficient distributed detection method for wireless sensor networks is considered in this paper. It is assumed that a group of sensors observe the same quantity in independent additive observation noises with possibly different variances. We assume that the links between the nodes and the fusion center are subject to Rayleigh fading wireless channels. At the fusion center, incorporating the effect of fading in the detection process, a decision fusion rule based on the linear minimum mean square error(LMMSE) is investigated. In this paper, two types of constraints are considered: 1) transmission power constraints at the nodes, and 2) the communication channel between the nodes and the decision center. we propose a optimal distributed estimation algorithm in energy-constrained wireless sensor networks. By numerical examples, we illustrate the performance of the proposed serial detection scheme.

A wireless sensor network (WSN) is a large collection of sensor nodes with limited power supply and constrained computational capability. Clustering routing method in wireless sensor networks has been considered as an important field of research recently to prolong the network lifetime of WSNs. We present a novel clustering method that can balance the energy consumption and extend the lifetime of WSN. Network nodes can be divided into densely connected subgroups through the algorithm of detecting community structure in complex networks. Moreover, the role of cluster-head is scheduled among the cluster members according to the residual energy of nodes, and then the cluster heads send the data to the sink directly. Based on the community clustering strategy, a novel routing protocol, called community structure clustering routing protocol (CSCR), has been raised for WSN. Performance evaluation has shown that the proposed method can achieve improvement compared with LEACH and SEP.

Target-synchronization of the distributed wireless sensor networks under the same sleeping–awaking method

Wireless sensor networks consist of thousands of sensor nodes that are battery-based and have a limited lifetime. Accordingly, performance and energy efficiency are big challenges in wireless sensor networks. In this regard, numerous techniques were studied and developed to reduce energy consumption. In this paper, a mathematical-based method has been proposed for the optimal selecting of the cluster head in wireless sensor networks. In the proposed algorithm, a node was selected as a cluster head that has the maximum energy, weight, and density, as well as the lowest total distance from the other nodes. In this respect, the problem was converted into a math function, which was solved by non-linear programming. The experiment results show that the presented algorithm is efficient, as compared with the other approaches that have, hitherto, been used to solve this problem.

A dynamic and multi-level key management method in wireless sensor networks (WSNs)

Wireless sensor networks have become an increasingly important area for research and application. Compared to traditional networks, its security faces many unfavorable factors such as severe resource constraints, inability to secure the wireless medium, potentially harsh sensing environment, etc. Attacks detection is an important issue to a wireless sensor network security. In this paper, sensors were classified and different kinds of malicious attacks in a wireless sensor network were analyzed, based on which a rule-based attacks detection method was proposed. The detection rules were given to detect most kinds of malicious attacks.

Aiming at the problem of poor encryption effect of the existing wireless sensor network privacy node encryption method, a wireless sensor network privacy node encryption method is designed based on edge computing. Firstly, the wireless sensor network structure and protocol stack structure are clarified, the composition structure of privacy nodes and the set of neighbour nodes are determined, and then the privacy node sensitivity is obtained through the adjacency matrix, and the privacy node location of wireless sensor network is realised by the trilateral positioning method. Finally, the edge computing method is introduced to aggregate the number of edge nodes of privacy nodes, complete the division of privacy node datasets, set terminal encryption keys for them, and realise wireless sensor network privacy node encryption. The experimental results show that the positioning error of this method is up to 0.9%, which can effectively improve the positioning accuracy of privacy nodes and improve the security of the network.

In wireless sensor network, the storage amount of information is high, and the transmission and scheduling of control information is reasonable. The node communication model, network structure model, and energy consumption model are constructed. On this basis, the high-density data in wireless sensor network are scheduled to optimize the time for nodes to perform tasks. The nodes in the network are fully scheduled to control the generation time of packets in the network and the generation time of packets in the network. Experimental results show that in different iterations, the proposed method has lower node delay and node energy consumption, with values less than 0.2 and 2, respectively, and the maximum data fusion quality can reach 98, with high fusion benefits, so as to improve the transmission and scheduling efficiency and quality of high-density data in wireless sensor network.

With recent technological advance, wireless sensor networks are often used in data collection and surveillance. One of the objectives of research on routing methods in wireless sensor networks is maximizing the energy life of sensor nodes that have limited energy. Among basic routing methods, the method using location information is efficient because it requires less information for calculation in route setting than flat routing and hierarchical routing. Because it utilizes distance, however, sensor nodes’ energy utility may go down. In this study, we tried to even energy use in a wireless sensor network by giving a weight to the transition probability of ACS(Ant Colony System), which is commonly used to find the optimal path, based on the amount of energy in a sensor and the distance of the sensor from the sink. The proposed method showed improvement by 46.80% on the average in energy utility in comparison with representative routing method GPSR (Greedy Perimeter Stateless Routing), and its residual energy after operation for a specific length of time was 6.7% more on the average than that in ACSKeywordsSensor NetworkRoutingEnergyHeuristic

Sensor localization is one of the supporting technologies in wireless sensor networks. The paper introduces the localization process of sensor nodes of DV-Hop algorithm. DV-Hop is one of the range-free localization algorithms using hop-distance estimate. An improved DV-Hop positioning algorithm uses grey correlation method to improve localization accuracy without needing an additional hardware device. Simulation results show that the improved DV-Hop algorithm outperforms the original DV-Hop algorithm and can provide more accurate location estimation.