The tiny and low-cost Sensors cannot simultaneously sense more than one channel since they do not have high-speed Analog-to-Digital-Convertors (ADCs) and high-power batteries. It is a critical problem when they are used for multi-channel sensing in cognitive Sensor networks (CSNs). One solution for this problem is that the Sensors sense various channels at different sensing periods. Due to the energy limitation in these scenarios, the lifetime maximization will become an important issue. In this paper, maximizing the lifetime of a CSN is investigated by selecting both the cooperative Sensors and their detector threshold, such that the desired detection performance constraints are satisfied. This is a NP-complete problem, and obtaining the optimum solution needs exhaustive search with exponential complexity order. Here we have proposed two convex-based optimization algorithms with low order of complexity. First algorithm applies the known instantaneous Signal-to-Noise-Ratio (SNR) and obtains the proper detector thresholds by solving an equation for every channel. Investigation the effect of detector thresholds on the energy consumption, the false alarm probability and the detection probability shows that we can minimize the detector thresholds such that the detection constraints are met. In the second algorithm in order to reduce the needed time for obtaining answers, the Bisection method is proposed for determining detector thresholds. Because knowing the instantaneous SNR is difficult, we have investigated the performance of the second algorithm by average value of SNR. Simulation results show that the proposed algorithms improve the performance of the network in case of lifetime and energy consumption.

In this paper, the problem of distributed state estimation of a nonlinear dynamical system in a decentralized Wireless Sensor Network (WSN) in the presence of state-dependent observation noise is considered. Some bearings or ranging devices, such as ultrasonic Sensors, have distance-dependent measurement error and their measurement noise variance grows as their relative distance to the target increases. This state-dependent measurement error leads to poor performance of estimation algorithm. To solve this problem, a consensus-based distributed state estimation methodology is presented in this paper by reaching a consensus on likelihood functions in the presence of state-dependent observation noise of bearings Sensors. To reduce energy consumption in WSN, a distributed Sensor Selection algorithm is proposed. Unlike centralized networks, no fusion center is deployed in decentralized networks to gather and process the collected data, globally. Moreover, there is no global knowledge of the network topology in decentralized networks. Therefore, the Posterior Cramér-Rao Lower Bound (PCRLB) is derived in a distributed fashion in the presence of state-dependent noise of bearings Sensors, to perform an adaptive Sensor Selection algorithm. Simulation results demonstrate the effectiveness of the proposed state estimation and Sensor Selection algorithms for a target tracking problem.

The necessity to tackle the increasing common concern about safety issues, urges the scientific community to come up with the development of innovative intruder detection and early warning security systems. One of the most effective technological solutions is provided by the application of WSNs. In this endeavor, most solutions have already adopted supercomputers and other computer resource systems to process the enormous amount of data. Alternatively, to this approach, simpler and more easily implementable solutions, such as the WSNmod method, are already being put to use. In particular, WSNmod is based on three key elements, the categorization of Sensor inputs, the quantization of the inputs and a time-window processing. WSNmod was introduced as an advanced intrusion detection system that focused on the minimization of the false positive alerts. Building on the idea of WSNmod, in this paper we focus, identify and quantify measurable parameters that influence the detection reliability. In addition, the very promising test results of the method and the security system are presented in a range of environmental conditions.

Wireless Sensor networks consist of a large number of Sensor nodes scattered over a limited geographical area. The main challenge of these networks is energy consumption. Clustering is a well-known way to save energy and extend network's lifetime. Many studies iteratively change the cluster formation to increase the network's lifetime, however, this issue imposes high energy consumption on clusters. Also, some clustering methods select individual cluster heads for near clusters, which leads to more energy consumption. Another major issue is selecting untrusted and unreliable nodes as headers because it leads to unreliable interactions between nodes and reduces the security of the network. The proposed method aims to provide an efficient clustering method that, in addition to having the benefits of energy consumption management, can provide a secure path for interaction and communication between nodes by identifying malicious nodes and not selecting them as headers. For this purpose, each node's chance is calculated using the fuzzy approach, and nodes that have the highest chances are considered cluster heads. The efficiency of the proposed method is compared with state-of-the-art methods. Also, the process of cluster formation is done by fuzzy logic and by defining the objective function consisting of residual energy, distance to the base station, and the average intra-cluster distance. The statistical analysis indicates that the proposed method on average provides better results than other competitors and the results demonstrate how this method at least improves life time and residual energy by 59. 83% and 14. 75%, respectively.

Wireless Sensor networks, which consist of numerous Sensor nodes deployed in a specific environment, have been the focus of extensive research for monitoring physical and environmental activities. The crucial component of a Sensor node is its battery power supply. As these batteries cannot be easily recharged in complex environments, achieving optimal energy consumption is a significant challenge for applications requiring a long network lifetime. Various methods have been proposed to address this challenge. One of most commonly used methods, is clustering of nodes and aggregation their data, as well as hierarchical transmission of sensed data to the base station. In this study, three types of network topologies—1) one-level, 2) two-level with maintaining dependence on the first layer cluster, and 3) two-level without dependence on the first layer cluster —were simulated using three methods of cluster head Selection: 1) random, 2) sorted, selecting nodes with higher energy , and 3) weight-oriented, based on the remaining energy of the nodes and the distance with other selected cluster heads. Selecting the optimal cluster head is an NP-complete algorithm, which entails high computational complexity for implementation. The simulation results demonstrate that selecting the cluster head based on the weight-oriented method in the assumed network types increases the network lifetime, reduces energy consumption, and prolongs the stability period, measured from network initiation to the first node failure.

The limited energy supply of wireless Sensor networks poses a great challenge for the deployment of wireless Sensor nodes. In this paper, a Sensor network of nodes with wireless transceiver capabilities and limited energy is considered. Clustering is one of the most efficient techniques to save more energy in these networks. Therefore, the proper Selection of the cluster heads plays important role to save the energy of Sensor nodes for data transmission in the network. In this paper, we propose an energy efficient data transmission by determining the proper cluster heads in wireless Sensor networks. We also obtain the optimal location of the base station according to the cluster heads to prolong the network lifetime. An efficient method is considered based on particle swarm algorithm (PSO) which is a nature inspired swarm intelligence based algorithm, modelled after observing the choreography of a flock of birds, to solve a Sensor network optimization problem. In the proposed energy-efficient algorithm, cluster heads distance from the base station and their residual energy of the Sensors nodes are important parameters for cluster head Selection and base station localization. The simulation results show that our proposed algorithm improves the network lifetime and also more alive Sensors are remained in the wireless network compared to the baseline algorithms in different situations.

Wireless Sensor network includes hundreds or even thousands of Sensor nodes, which are devices with low energy and often impossible to be replaced or recharged. For this reason, efficient use of energy is one of the major challenges in these networks. One way for increasing energy efficiency in such networks is clustering. Clustering protocols such as LEACH, TEEN, SEP and DEEC randomly select the cluster heads for the clustering process. This causes the cluster heads to be close together, decreasing the efficiency of protocols. For this reason, we proposed a new approach called EBDCH. In this method, when cluster heads are close together, we distribute them based on the energy situation. The proposed approach runs on the clustering protocols. The simulation results show that with the implementation of the proposed method, throughput is increased and network lifetime is improved compared to protocols LEACH, DEEC, SEP and EEHC respectively at 26%, 28%, 37% and 36%.