The focus is on the longevity and energy efficiency of wireless sensor networks (WSN). WSNs face many obstacles in terms of data transmission. WSNs face difficulties in reducing energy output and shortening life cycles, including node configuration, leader selection, and optimal routing selection. The provisioning of nodes, selection of cluster leaders, and optimal paths have all been recommended using many current methods. However, none of the currently used methods yield sufficient grid energy optimization results. Therefore, this study proposes a modified Moth Flame Optimization Algorithm (MFOOA). Nature passed it on to us. The main inspiration for this optimizer is the lateral flight pattern used by moths in nature. At night, the moth maintains a constant angle to the moon. This is a particularly efficient way to drive long distances in a straight line. Nevertheless, artificial light is everywhere around these amazing creatures, encircling them in a fruitless and deadly spiral. Here, this behavior is theoretically modeled for optimization. The suggested program places the sensor nodes using the flame optimization technique. These sensor nodes might be either dynamic or static depending on the network scenario. The cluster head and the optimum route are chosen using this technique. Within the predetermined search space, it also does phase balancing between the exploration and development phases. In terms of residual energy, sensor node lifetime, used energy, end-to-end latency, and a maximum number of cycles, it differs from current classical and swarm intelligence (SI) techniques. According to the results, MFOOA is superior to its counterpart.

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.

Today, wireless sensor networks are widely employed in various applications, including monitoring environments and tracking objects for military surveillance, industrial applications, and healthcare. Thus, the establishment of security in such networks is of great importance. One of the dangerous attacks against these networks is the Sybil attack. In this attack, a malicious node propagates multiple fake identities simultaneously, which affects routing protocols and many other operations like voting, reputation evaluation, and data aggregation. In this paper, we study the sensor node rate trust decision to defend against Sybil attacks in WSNs and its dynamics that play a key role in stabilizing the whole WSN using evolutionary game theory. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply the theoretical foundations to devise defiance against Sybil attacks in WSNs. Moreover, we can find out the conditions that will lead SNs to choose the strategy Healthy as their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a rate-trust mechanism to satisfy these conditions. And furthermore, the efficiency of algorithms in terms of true detection rate and false detection rate is evaluated through a series of experiments. Experiment results show that the proposed algorithm is able to detect 99.9% of Sybil nodes with a 0.005% false detection rate. Additionally, the proposed algorithm is compared with other algorithms in terms of true detection rate and false detection rate, which shows that the proposed algorithm performs satisfactorily.

Sensors in WSN work with batteries that have limited energy capacity. Therefore, reduction in power consumption is a very important issue. In this paper, we present a new routing algorithm to reduce power consumption in wireless sensor networks. This algorithm deploys Learning automata in each node to find a suitable path for routing data packets. In order to aim this goal the algorithm uses penalty based approach in learning automata and considers energy level of nodes and latency of packet delivery as well.Performance of our new developed algorithm has been compared with LABER and BEAR protocols in OMNET++ simulator. Simulation results show that, in a network with static nodes, energy consumption and control packets reduce significantly and network lifetime increases in comparison with two other protocols.

Improving the Sensor Network Lifetime through evolutionary routing protocols is one of the largest research interests. To maximize this WSN feature, the data and message delivery routes are selected in such a way that the total energy consumption is minimized. The purpose of this paper is to analyze a detailed comparison between two typical WSN protocols and their impacts over the WSN lifetime. In order to achieve to this objective, several main keys and factors such as the first dead sensor, remaining energy and transmission range are considered. The experiment results showed remarkable outcomes and confirmed that the flat and cluster-based protocols can increase WSN lifetime in different ways.

Wireless sensor networks are attracting a great deal of research interest. One of the most interesting researches in these networks is localization of sensor nodes whose accurate is a strong requirement in many applications. In recent years, several techniques have been proposed for localization in wireless sensor networks. In this paper, we present a localization scheme using only one mobile anchor station with receiving signal strength indicator technique, which reduces average localization errors and execution time. The satisfactory simulation results and also comparison of localization errors and execution time between our scheme and similar previous schemes depicts the efficiency of proposed method against previous schemes.

One of the major challenges in wireless sensor networks is how to transfer information from nodes within the network to the base station and choose the best possible route to transmit this information. Choosing the best route can be affected based on various factors such as energy consumption, response speed and latency, data transfer accuracy, and so on. Our goal is to choose the best route in terms of energy consumption. in this paper, an optimal method using tools such as multi-objective particle swarm optimization algorithm, environment segmentation, and node location search is presented to increase energy efficiency. In the proposed method, a binary particle swarm optimization algorithm is used to locate the nodes distributed in the wireless sensor networks and uses the positioning error and computation time during the simulations as a performance criterion. In the proposed method to meet the different needs of the users regarding the QoS network, a multi-constrained QoS routing model is launched and the fit function is constructed by converting the QoS constraints to the penalty function. We also introduced the ideas of natural selection and GA mutation for PSO to improve the performance of the PSO algorithm, which led to a greater variety of particles. The results show that the proposed method can solve the CAB examples very effectively and efficiently and provide better computational results than the PSO. The simulation results showed that in the proposed method the computational time required for locating decreased by 59.8% and the locating error increased by 3.2%. By reducing the computational time for locating, the energy saving and lifetime of the wireless sensor network could be increased. It should be noted, however, that the success rate of the algorithm also increased by about 36% to 54% in algorithms with similar structures.