Energy and Spectrum Efficient Wireless Sensor Networks

1Department of Electrical, Electronic and Information Engineering, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy 2Mathematical and Algorithmic Sciences Lab, France Research Center, Huawei Technologies Co. Ltd., 20 quai du Point du Jour, 92100 Boulogne-Billancourt, France 3Department of Information Engineering, University of Florence, Via di Santa Marta 3, 50139 Firenze, Italy 4BISITE Research Group, University of Salamanca, Edificio I+D+i, C/Espejo, 37007 Salamanca, Spain 5Department of Electronic Engineering, Tsinghua University, Beijing 100084, Chin

Un nuevo esquema de agrupación para redes sensoras inalámbricas de radio cognitivas heterogéneas

Introduction: This article is the product of the research “Learning-based Spectrum Analysis and Prediction in Cognitive Radio Sensor Networks”, developed at Sejong University in the year 2019. Problem: Most of the clustering schemes for distributed cognitive radio-enabled wireless sensor networks consider homogeneous cognitive radio-enabled wireless sensors. Many clustering schemes for such homogeneouscognitive radio-enabled wireless sensor networks waste resources and suffer from energy inefficiency because of the unnecessary overheads. Objective: The objective of the research is to propose a node clustering scheme that conserves energy and prolongs network lifetime. Methodology: A heterogeneous cognitive radio-enabled wireless sensor network in which only a few nodes have a cognitive radio module and the other nodes are normal sensor nodes. Along with the hardware cost, theproposed scheme is efficient in energy consumption. Results: We simulated the proposed scheme and compared it with the homogeneous cognitive radio-enabled wireless sensor networks. The results show that the proposed scheme is efficient in terms of energyconsumption. Conclusion: The proposed node clustering scheme performs better in terms of network energy conservation and network partition. Originality: There are heterogeneous node clustering schemes in the literature for cooperative spectrum sensing and energy efficiency, but to the best of our knowledge, there is no study that proposes a non-cognitiveradio-enabled sensor clustering for energy conservation along with cognitive radio-enabled wireless sensors. Limitations: The deployment of the proposed special device for cognitive radio-enabled wireless sensors is complicated and requires special hardware with better battery powered cognitive sensor nodes

Spread Spectrum based QoS aware Energy Efficient Clustering Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs) are composed of small, resource-constrained sensor nodes that form self-organizing, infrastructure-less, and ad-hoc networks. Many energy-efficient protocols have been developed in the network layer to extend the lifetime and scalability of these networks, but they often do not consider the Quality of Service (QoS) requirements of the data flow, such as delay, data rate, reliability, and throughput. In clustering, the probabilistic and randomized approach for cluster head selection can lead to varying numbers of cluster heads in different rounds of data gathering. This paper presents a new algorithm called "Spread Spectrum based QoS aware Energy Efficient Clustering for Wireless sensor Networks" that uses spread spectrum to limit the formation of clusters and optimize the number of cluster heads in WSNs, improving energy efficiency and QoS for diverse data flows. Simulation results show that the proposed algorithm outperforms classical algorithms in terms of energy efficiency and QoS

Ameliorate the performance using soft computing approaches in wireless networks

Wireless sensor networks are an innovative and rapidly advanced network occupying the broad spectrum of wireless networks. It works on the principle of “use with less expense, effort and with more comfort.” In these networks, routing provides efficient and effective data transmission between different sources to access points using the clustering technique. This work addresses the low-energy adaptive clustering hierarchy (LEACH) protocol’s main backdrop of choosing head nodes based on a random value. In this, the soft computing methods are used, namely the fuzzy approach, to overcome this barrier in LEACH. Our approach’s primary goal is to extend the network lifetime with efficient energy consumption and by choosing the appropriate head node in each cluster based on the fuzzy parameters. The proposed clustering algorithm focused on two fuzzy inference structures, namely Mamdani and Sugeno fuzzy logic models in two scenarios, respectively. We compared our approach with four existing works, the conventional LEACH, LEACH using the fuzzy method, multicriteria cluster head delegation, and fuzzy-based energy efficient clustering approach (FEECA) in wireless sensor network. The proposed scenario based fuzzy LEACH protocol approaches are better than the four existing methods regarding stability, network survivability, and energy consumption

Clustering in Multi-Channel Cognitive Radio Ad Hoc and Sensor Networks

© 1979-2012 IEEE. CR enables dynamic spectrum access to utilize licensed spectrum when it is idle. CR technology is applied to wireless ad hoc and sensor networks to form CRAHNs and CRSNs, respectively. Clustering is an efficient topology management technique to regulate communication and allocate spectrum resources by CR capabilities of nodes in CRAHNs and CRSNs. In this article, we thoroughly investigate the benefits and functionalities of clustering such as topology, spectrum, and energy management in these networks. We also overview motivations for and challenges of clustering in CRAHNs and CRSNs. Existing clustering schemes are reviewed and compared. We conclude by revealing key considerations and possible solutions for spectrum-aware clustering in multi-channel CRAHNs and CRSNs

Fuzzy based clustering in CWPSN using machine learning model

90-94Cognitive wireless power sensor network (CWPSN) technology, widely used in almost all fields, has addressed various issues. The researchers have addressed the problems in the lack of radio spectrum availability and enabled the allocation of dynamic spectrum access in specific fields. The main challenge has been to support the radio spectrum allocation using intelligent adaptive learning and decision-making techniques so that various requirements of 5G wireless networks can be encountered. Machine learning (ML) is one of the most promising artificial intelligence tools conceived to support cognitive wireless networks. This paper aims to provide energy optimization and enhance security to cognitive wireless power sensor networks using a novel protocol during resource allocation. In addition to the existing methods, a novel protocol, fuzzy cluster-based greedy algorithms for attack prediction and energy harvesting using a machine-language model based on neural network techniques have been introduced. The simulation has been done using MATLAB software tools which gives efficient results

Energy Efficient Design of Wireless Ad Hoc Networks

The concept of wireless is not new. When the packet switching technology, the fabric of the Internet was introduced by the Department of Defense, the ARPANET ,it understood the potential of packet switched radio technology to interconnect mobile nodes .The DARPA around early 70’s helped establish the base of ad hoc wireless networking. This is a technology that enables untethered wireless networking environments where there is no wired or cellular infrastructure. Wireless Ad hoc Networks since then is a fast developing research area with a vast spectrum of applications. Wireless sensor network systems enable the reliable monitoring of a variety of environments for both civil and military applications. The Energy efficiency continues to be a key factor in limiting the deployability of ad-hoc networks. Deploying an energy efficient system exploiting the maximum lifetime of the network has remained a great challenge since years. The time period from the instant at which the network starts functioning to the time instant at which the first network node runs out of energy, i.e. the network lifetime is largely dependent on the system energy efficiency. This thesis looks at energy efficient protocols, which can have significant impact on the lifetime of these networks. The cluster heads get drain out maximum energy in the wireless ad hoc networks. The proposed algorithm deals with minimizing the rate of dissipation of energy of cluster heads. The algorithm LEAD deals with energy efficient round scheduling of cluster head followed by allocation of nodes to the cluster heads maximizing network lifetime using ANDA

Defeating Jamming Attack in Wireless Ad-Hoc Networks using Puzzle Based Hashing Technique

The growth of wireless technologies, jamming in wireless sensor network is a major problem in network communication. Jamming attacks is a denial of service (DoS) attack in network to block legitimate communication. Wireless networks drop multiple security threats. Transceiver can intrude on wireless transmissions under cryptographic methods. In jamming, the message is transmitted as continuous transmission of high signals. These strategies have many disadvantages. We have to increase the energy of jam frequency and easy to detect attacks. Normal anti-jamming techniques depend on spread-spectrum communications. This technique is used to secure only wireless transmission. To deeply understand this above problem, we need to analyze in detailed manner. Finally we discuss open issues in this network, such as energy efficient saving scheme, packet classification and packet dropping