A Novel Data Query Method for Wireless Sensor Networks

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.

Energy efficiency is a critical factor for operation of a wireless sensor network. In general, wireless sensor networks perform clustering and routing using localized neighbor information and simple rules only. In this paper, we propose an energy efficient clustering and routing method for wireless sensor networks by exploiting energy state information of neighbor sensor nodes for selection of cluster heads. We compared the performance of the proposed method with that of a well known clustering and routing protocol. Our computational experiments demonstrate that the lifetime and distribution of energy consumptions of the proposed method are better than those of the compared method.

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.

The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the sink. By utilizing Intel Berkeley Research Lab (IBRL) dataset, the efficiency of the proposed method was measured. The experimental findings illustrate the benefits of the proposed method as it reduces the overhead on the sensor node level up to 1.25% in remaining data and reduces the energy consumption up to 93% compared to prefix frequency filtering (PFF) and ATP protocols.

Past decades have witnessed the advancement of wireless sensor networks (WSNs) in both academic and industrial communities. Clustering is one of the most popular methods to increase the lifespan of WSNs. The optimal number of cluster heads and how to organize the clusters are the most important issues to be addressed in the clustering methods. In this paper, we proposed a novel user-independent and dynamical method to calculate the optimal number of clusters, organize the clusters, and determine the best cluster heads in each round. In this method, efficient energy consumption and link quality were considered to compute the optimal number of clusters. Then, the algorithm began to organize the compact clusters with high energy level cluster heads. We investigated a new fitness function in order to achieve these objectives. A new version of gravitational search algorithm (GSA) was used to solve this optimization problem. In this algorithm, the power distance sums scaling method was applied to calculate the mass values. Then, a fuzzy logic controller is employed to identify the parameter of this algorithm to control the exploitation and exploration abilities of the method during the computational process of the algorithm. Then, the novel version of GSA was applied to reach an appropriate solution for the fitness function, find the optimal number of clusters, and properly organize these clusters. To evaluate the effectiveness of the proposed method, several experiments were performed and the obtained results were compared with the results of other popular clustering methods. The simulation results revealed that the performance of the modified GSA was better than other state-of-the-art meta-heuristic optimization algorithms. Moreover, the proposed method for the clustering problem in WSNs outperformed other popular clustering methods and increased the lifetime of WSNs.

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.

Aimed at the demands of wireless sensor networks for high energy-efficient time synchronization, the reduction of synchronization energy consumption is studied from the aspects of both accurate timestamps marking and synchronous information transmission mechanism. First, the network is divided into several parent–child groups periodically. The group-wise pair selection algorithm is used to select the network’s pairwise synchronization nodes, and chain-type network topology is thus generated. Second, the sequential multi-hop synchronization algorithm is introduced to realize the synchronization information exchange among pairwise synchronization nodes. The overhearing synchronization (OS) nodes obtain the synchronization information packet based on a one-way overhearing mechanism. Moreover, the accurate acquisition of the synchronization ack packet’s timestamp is carried out through the use of receiving-time-plus-fixed-delay mode. Third, the joint maximum likelihood method and the minimum variance unbiased estimation method are used to estimate the clock offsets of pairwise synchronization nodes and overhearing nodes to the parent nodes, respectively, based on which the child nodes adjust their local virtual clocks. Periodically, the pairwise synchronization nodes initiate the network’s time synchronization, estimate, and broadcast the relative offset to the gateway node, assisting the upper layer child nodes in synchronizing to the gateway node. Simulation results show that the proposed method not only achieves the millisecond level synchronization accuracy but also reduces the synchronization energy consumption and thus improves the network lifetime.

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 distributed energy-efficient coverage holes detection and recovery method in wireless sensor networks using the grasshopper optimization algorithm

A queue-based prolong lifetime methods for wireless sensor node

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

This paper presents a novel query optimization method for wireless sensor networks. By taking advantage of the delay constraint specified with a query, the method can find the optimal combinations of sending sensor data from nodes to sink, with or without performing data processing. System models for cost and timing of both computation and communication are created. Conditions of performing optimization are described accordingly. Three cases of possible optimization are discussed. Finally, performance of the proposed method is evaluated. It shows that the proposed method achieves better performance than without performing the optimization.

Properly adjusting the transmission power of the wireless sensor nodes has shown to be an effective approach to reduce energy consumption and to improve the network reliability. I In this paper, an adaptive Node Degree adjustment based Power Control method for wireless sensor networks (NDPC) is proposed to dynamically adjust the transmission power of the nodes. In NDPC, each node is embedded with a fuzzy neural controller which is used to adjust the target node degree so as to control the communication range properly. The fuzzy neural controller consists of two Fuzzy Inference Systems (FIS). One adopts closed-loop feedback mechanism to adjust the target node degree according to the residual energy. The other adjusts the communication range by a self-learning neural network to control the transmission power based on the target node degree. Consequently, the actual energy consumption of the node is reduced while keeping the desired node degree. Several simulation experiments are conducted to evaluate the performance of NDPC, and the results show that NDPC can reduce the actual energy consumption as well as extend the network lifetime.

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.