Research on wireless sensor network (WSN) has increased tremendously throughout the years. In WSN, sensor nodes are deployed to operate autonomously in remote environments. Depending on the network orientation, WSN can be of two types: flat network and hierarchical or cluster-based network. Various advantages of cluster-based WSN are energy efficiency, better network communication, efficient topology management, minimized delay, and so forth. Consequently, clustering has become a key research area in WSN. Different approaches for WSN, using cluster concepts, have been proposed. The objective of this paper is to review and analyze the latest prominent cluster-based WSN algorithms using various measurement parameters. In this paper, unique performance metrics are designed which efficiently evaluate prominent clustering schemes. Moreover, we also develop taxonomy for the classification of the clustering schemes. Based on performance metrics, quantitative and qualitative analyses are performed to compare the advantages and disadvantages of the algorithms. Finally, we also put forward open research issues in the development of low cost, scalable, robust clustering schemes.
New wireless network paradigms will demand higher spectrum use and availability to cope with emerging data-hungry devices. Traditional static spectrum allocation policies cause spectrum scarcity, and new paradigms such as Cognitive Radio (CR) and new protocols and techniques need to be developed in order to have efficient spectrum usage. Medium Access Control (MAC) protocols are accountable for recognizing free spectrum, scheduling available resources and coordinating the coexistence of heterogeneous systems and users. This paper provides an ample review of the state-of-the-art MAC protocols, which mainly focuses on Cognitive Radio Ad Hoc Networks (CRAHN). First, a description of the cognitive radio fundamental functions is presented. Next, MAC protocols are divided into three groups, which are based on their channel access mechanism, namely time-slotted protocol, random access protocol and hybrid protocol. In each group, a detailed and comprehensive explanation of the latest MAC protocols is presented, as well as the pros and cons of each protocol. A discussion on future challenges for CRAHN MAC protocols is included with a comparison of the protocols from a functional perspective.
Cognitive radio sensor networks (CRSNs) are multi-channel-capable networks that inherit some of the challenges of traditional wireless sensor networks (WSNs), such as limited power source and hardware capacity. In several CRSN applications, such as surveillance and intelligent transportation systems, node mobility is a typical assumption. However, as a node changes its physical location, spectrum mobility may also follow. Therefore, the treating of node mobility in CRSN imposes new challenges on all network layers, especially in the data link layer. In this paper, we propose a novel cross-layer mobility-aware medium access control (MAC) protocol for CRSN. We also propose an efficient spectrum-aware cluster formation and maintenance. The proposed scheme is more robust against primary users' activity as well as node mobility in a CRSN because it integrates spectrum sensing at the physical (PHY) layer with packet scheduling at the MAC layer. Simulation results show that the proposed protocol guarantees about 60 % more common channels per cluster in a higher node ratio. Moreover, the proposed MAC protocol outperforms existing protocols (e.g., CogMesh, cluster-based MAC, and KoN-MAC) in terms of the packet delivery ratio, energy consumption, and delay, by up to 5, 30, and 25 %, respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations鈥揷itations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.