To prolong the function of wireless sensor networks (WSNs), the lifetime of the system has to be increased. WSNs lifetime can be calculated by using a few generic parameters, such as the time until the death of the first node and other parameters according to the application. Literature indicates that choosing the most appropriate cluster head by clustering is one of the most successful ways to improve the lifespan of the WSN. The drawback of clustering protocols is based on the probabilistic model. Sometimes they select two cluster heads for two different clusters which are very close to each other and results in head situated at the edge of the cluster in some cases. This type of cluster head selection leads to a reduction in energy efficiency. Therefore, we have proposed the LEACH-Fuzzy Clustering (LEACH-FC) protocol and implemented a fuzzy logic-based cluster head selection and cluster formation to maximize the lifetime of the network. For selections of cluster head and formation of the cluster, we have used a centralized approach instead of distributed ones. We have also employed fuzzy logic in the selection of vice cluster head, which is also a centralized approach. The proposed algorithm has been found to be effective in balancing the energy load at each node thereby enhancing the reliability of WSN. It outperforms other proposed algorithms for improving network lifetime and energy consumption.

Wireless Sensor Network (WSN) is an innovative technology with a broad range of applications and highly attractive benefits, such as low cost of implementation and data transmission, unmonitored access to the network, autonomous and long-term operation. With extensive demand for the advancement of related technologies (cloud computing, near-field communications and cellular mobile networks), the Internet of Things (IoT) is becoming a very exciting paradigm. By using communication technologies in sensors and sensing features in web devices, WSNs have begun interaction with the Iot devices. IoT provides access to a large amount of information gathered by WSNs. However, the security of WSN and IoT comes at a cost, mainly due to privacy management issues. Therefore, this paper offers a comprehensive analysis of security threats against WSN and IoT, along with the strategies for preventing, detecting and mitigating those threats. The related defense mechanisms can help in building a safe IoT expansion and widespread understanding by getting familiar with the details of these attacks. The aim of this paper is to address and demonstrate the impact of the security problems on WSNs from the viewpoint of the IoT and its applications. In the analysis carried out for this work, a classification of available attacks and threats against these requirements has also been included.

In case of no fixed infrastructure (military applications and emergency rescue operations) and we need to build a network with low cost, Wireless sensor networks (WSNs) are useful. We have no fixed routing protocol, or intrusion detection technique available for them because WSNs are dynamic in nature and individual nodes of the network are required for this to be done. Nodes are mobile in most of the applications of WSNs, so they depend on battery power and availability of limited resources which shows that power consumption is an effective research area for performing a set of tasks in WSNs. To deal with such an issue, machine learning (ML) techniques (self-learning algorithms, working without programming or human intervention) can be applied effectively according to the application requirement. In this paper, we have done comparative about several ML-based techniques for WSNs. In addition, we also analyzed ML techniques for clustering and energy harvesting. At the end, we present a summary of ML techniques for both clustering and energy harvesting with some open issues.

Energy efficiency is a key parameter in the design of wireless sensor networks (WSNs). Sensor nodes are generally operated by using batteries of limited capacity. To achieve energy efficiency, a reduction in radio communication is necessary. The communication unit consumes the major power in a wireless sensor node. Reduction in power consumption to make reliable wireless sensor networks, due to the exchange of data between sensor nodes can be attained with the help of data compression approach. A lossless data compression (LDC) approach is proposed to attain reliability in wireless sensor networks. Compression schemes have advantages that they can allow dynamical adjustment for the change in source data statistics for performance maximization. The proposed algorithm requires less computational power and its effectiveness has been computed by compression of temperature and relative humidity data sets gathered by real WSNs. The compression ratio has been obtained up to 73.54% and 61.82% for temperature and relative humidity data sets.

Wireless sensor networks (WSNs) are becoming very common in numerous manufacturing industries; especially where it is difficult to connect a sensor to a sink. This is an evolving issue for researchers attempting to contribute to the proliferation of WSNs. Monitoring a WSN depends on the type of collective data the sensor nodes have acquired. It is necessary to quantify the performance of these networks with the help of network reliability measures to ensure the stable operation of WSNs. Reliability plays a key role in the efficacy of any large-scale application of WSNs. The communication reliability in a wireless sensor network is an influential parameter for enhancing network performance for secure, desirable, and successful communication. The reliability of WSNs must incorporate the design variables, coverage, lifetime, and connectivity into consideration; however, connectivity is the most important factor, especially in a harsh environment on a large scale. The proposed algorithm is a one-step approach, which starts with the recognition of a specific spanning tree only. It utilizes all other disjoint spanning trees, which are generated directly in a simple manner and consume less computation time and memory. A binary decision illustration is presented for the enumeration of K-coverage communication reliability. In this paper, the issue of computing minimum spanning trees was addressed and it is a pertinent method for further evaluating reliability for WSNs. This paper inspects the reliability of WSNs and proposes a method for evaluating the flow-oriented reliability of WSNs. Further, a modified approach for the sum-of-disjoint products to determine the reliability of WSN from the enumerated minimal spanning trees is proposed. The proposed algorithm when implemented for different sizes of WSNs demonstrates its applicability to WSNs of various scales. The proposed methodology is less complex and more efficient in terms of reliability.