Sachin Gajjar

Cluster Head Selection Protocol using Fuzzy Logic for Wireless Sensor Networks

trends in field of wireless networks is setting up Wireless Sensor Networks that, senses specified parameter(s) related to environment; processes sensed data and wirelessly communicates it to a base station. Such networks open up a whole new range of applications, including precision agriculture, monitoring and tracking vehicles, animals and humans, battle-field surveillance, civil structural monitoring etc. All these applications require extended network lifetime, scalability, and traffic balancing among nodes in the network. Clustering is one of the effectual techniques for achieving these requirements. In clustering, geographically adjacent nodes are organized into virtual groups called clusters. One of the cluster node acts as a cluster head and rest as cluster members. This paper presents Cluster Head selection protocol using Fuzzy Logic (CHUFL). It uses nodes parameters like: residual energy, reachability from its neighborhood, quality of communication link with its neighborhood and distance from base station as fuzzy input variables for cluster head selection. A comparative analysis of CHUFL with cluster head selection mechanism using fuzzy logic by Indranil et. al.; Cluster Head Election mechanism using Fuzzy logic (CHEF) by Kim et. al. and cluster head selection method for wireless sensor networks based on fuzzy logic by J. Anno et. al. shows that CHUFL is up to 20 % more energy efficient and sends 72% more packets to base station compared to protocol by J. Anno et. al., one of the energy efficient clustering protocol.

FAMACROW: Fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks

Abstract This paper presents Fuzzy and Ant Colony Optimization Based Combined MAC, Routing, and Unequal Clustering Cross-Layer Protocol for Wireless Sensor Networks (FAMACROW) consisting of several nodes that send sensed data to a Master Station. FAMACROW incorporates cluster head selection, clustering, and inter-cluster routing protocols. FAMACROW uses fuzzy logic with residual energy, number of neighboring nodes, and quality of communication link as input variables for cluster head selection. To avoid hot spots problem, FAMACROW uses an unequal clustering mechanism with clusters closer to MS having smaller sizes than those far from it. FAMACROW uses Ant Colony Optimization based technique for reliable and energy-efficient inter-cluster multi-hop routing from cluster heads to MS. The inter-cluster routing protocol decides relay node considering its: (i) distance from current cluster head and that from MS (for energy-efficient inter-cluster communication), (ii) residual energy (for energy distribution across the network), (iii) queue length (for congestion control), (iv) delivery likelihood (for reliable communication). A comparative analysis of FAMACROW with Unequal Cluster Based Routing [33] , Unequal Layered Clustering Approach [43] , Energy Aware Unequal Clustering using Fuzzy logic [37] and Improved Fuzzy Unequal Clustering [35] shows that FAMACROW is 41% more energy-efficient, has 75–88% more network lifetime and sends 82% more packets compared to Improved Fuzzy Unequal Clustering protocol.

Comparative analysis of wireless sensor network motes

Wireless Sensor Network (WSN) applications range from simple data gathering to hard to imagine fields like Internet of Things. For all the applications the physical design space consists of sensors extended with storage, power supply, computation and communication capabilities, the so-called motes. These motes run the network protocol programs that most of the time sleep, and occasionally collect, process, store and communicate the data to Base Station (BS). The number of protocol proposals has increased but unfortunately the number of mote studies has not. This paper discusses the essential subsystems of motes, surveys and does a comparative analysis of well-known motes. The motes are selected based on a number of criteria including popularity, published results, interesting characteristics and features. The motes are analyzed using a number of different parameters and criteria, including processor used, lifetime, cost, software support, size, their strengths and weaknesses. Simulation of LEACH protocol using motes discussed in the paper is carried out and a comparative analysis of network lifetime, data transmitted and energy consumption of network is presented. The goal of work is to aid WSN application developers to select appropriate mote for their network or determine features that should be included on a custom built sensor node platform.

Wireless Sensor Network: Application led research perspective

A Wireless Sensor Network is a network of sensors that, senses specified parameter(s) related to environment; processes data locally or in a distributed manner and wirelessly communicates information to central Base Station. The Base Station analyzes information and initiates suitable response if required. Wireless Sensor Network research as a whole suffers a lack of practical application scenarios for which such networks are the best solution. Researchers generally do not emphasize on the application domains they are trying to address. Therefore they cannot accurately assess the efficiency of their proposal because for different application areas there are different technical issues. This paper discusses role of application in research and fleshes out from the literature applications of sensor networks ranging from billion dollar satellites to tiny RF tags. To aid in application led research we demonstrate that different applications take different directions in the design goals. Based on this observation the sensor network design goals and its various directions are characterized. Such explicit design direction works as a framework for discussing and structuring coordinated research (e.g., scrutinizing mutual dependencies between applications, software, hardware and hence avoiding duplicate work). It also provides a conceptual basis for the development of flexible software and hardware frameworks that can be adapted to meet different application needs.

Performance analysis of cross layer protocols for wireless sensor networks

Present age technologies like Micro-Electro-Mechanical Systems for development of smart sensors, small transceivers and low-priced hardware are fueling increased interest in Wireless Sensor Networks. The task of developing a generic protocol framework for optimizing Wireless Sensor Networks is challenging because limited processing capabilities, memory and power supply of sensor node make it difficult to cater requirements of versatile applications of these networks. This has forced researchers to dissect the traditional layered protocol design process. As a result cross layer protocols that attempt to exploit richer interaction among communication layers to achieve performance gains have emerged. This paper surveys, classifies, simulates using Network Simulator NS2 and analyzes well referred cross layer protocols namely Low Energy Adaptive Clustering Hierarchy, Self Organized TDMA Protocol, Flexible TDMA Protocol, Energy Efficient Fast Forwarding Protocol and D-MAC. We also identify possible risks associated with cross layer design and suggest precautionary guidelines for the same.

Cross layer architectural approaches for Wireless Sensor Networks

A Wireless Sensor Network is a network of sensors that senses specified parameter(s) related to environment; processes data locally or in a distributed manner and wirelessly communicates information to central Base Station. The Base Station analyzes information and initiates suitable response if required. The complexities introduced by severely limited processing capabilities, memory and power supply in the sensor node at one end and the design needs of versatile applications at the other end have forced researchers to dissect the layered protocol design process. As a result cross layer approaches which attempt to exploit a richer interaction among communication layers to achieve performance gains have emerged. Cross-layer interactions offer the possibility of dealing with the special properties of Wireless Sensor Network that cannot be handled well by layered architectures. For example, this can be handling the variations in link quality or adjusting the radio strength, which influences the transmission range, the number of collisions, and energy consumption. These parameters can further be used as routing metrics at Network Layer for efficient routing. This paper surveys cross layer architectures, presents performance benchmarking for the architectures and finally compares them on the basis of the introduced benchmarks.

Improving performance of adhoc TCP in Mobile Adhoc Networks

Modifying transmission control protocol (TCP) to improve its performance in wireless networks has been a long-standing research problem. Many methods have been proposed to improve TCPpsilas performance in mobile adhoc networks (MANETs). Among them ad hoc TCP (ADTCP) uses an end-to-end approach which requires minimal changes at the sender and receiver, provides the flexibility for backward compatibility, maintains end-to-end TCP semantics and is TCP-friendly. It uses end-to-end measurements to detect congestion, disconnection, route change, and channel error, and each detection result triggers corresponding control actions. This paper proposes improved-ADTCP (I-ADTCP) an improvement on ADTCP. To improve the performance of ADTCP we consider following: ensure sufficient bandwidth utilization of the sender-receiver path; avoid the overloading of network by limiting TCPpsilas congestion window below the upper bound of bandwidth delay product (BDP-UB) of the path; check for incipient congestion by calculating inter-packet delay difference and short term throughput using relative sample density (RSD) technique. In an incipient congestion condition, we reduce congestion window limit (CWL) to half, which limits the packets send by the sender and does not allow the congestion to build up. Thus the algorithm tries to remain in congestion avoidance phase at all times by detecting and reacting to incipient congestion. Results of simulation using NS-2 show that I-ADTCP performance is superior to that of ADTCP for all levels of traffic intensity in the network. The good put performance is improved by 10%-30% in I-ADTCP.

FUCP: Fuzzy based unequal clustering protocol for wireless sensor networks

This paper presents Fuzzy Based Unequal Clustering Protocol (FUCP) for wireless sensor networks. The cluster head selection mechanism uses fuzzy logic with three node descriptors namely, residual energy, centerness with respect to its neighbor, and quality of communication link with its neighbors for cluster head selection. To avoid hot spots and for uniform network traffic distribution, FUCR uses unequal clustering. For this, fuzzy logic is used with node distribution and distance from master station to decide number of cluster heads and cluster head advertisement radius in a given area. A comparative analysis of FUCR, Low Energy Adaptive Clustering Hierarchy, Hybrid Energy Efficient Distributed Clustering, Cluster Head Election mechanism using Fuzzy logic, and Distributed Energy Efficient Hierarchical Clustering shows that FUCP is up to 40% more energy efficient, has 31% more network lifetime, and sends 57% more packets to master station compared to Distributed Energy Efficient Hierarchical Clustering.

Fuzzy-Cross: A fuzzy based architecture for wireless sensor network

This paper introduces Fuzzy-Cross, a decision making and information-sharing architecture for wireless sensor networks (WSN)that enables the protocols to achieve energy efficiency, reliability,and low data latency. FUCRretains a layered structure with each layer matching to a communication function to provide a practical and simple design. The administrative plane provided by FUCR collects residual energy from physical layer, data delivery, and channel assessment records from data link layer, packet dispatch rate from network layer and sensitivity of sensing unit from application layer. The information collected is used as input descriptors for running fuzzy logic. Output of the fuzzy logic assists physical layer protocol to decide nodes transmit power; data link layer protocol to decide retransmission time out, back off time and duty cycle; network layer to determine chance of the node to become a relay node and application layer to determine chance of node to become a reporting node. To investigate the extent to which Fuzzy-Cross meets its goals, it is implemented on top of ZigBee standard. Simulation results demonstrated that Fuzzy-Cross with ZigBee outperforms both (i) ADaptive Access Parameters Tuning (ADAPT) with Zigbee and (ii) standard ZigBee without any modifcations. For a single hop network Fuzzy-Cross is up to 12% more energy efficient compared to ADAPT. Delivery ratio of Fuzzy-Cross is up to 12% more and data latency is up to 19% less compared to ADAPT. Similar trend is seen for multi-hop network and for a wide range of operating conditions.

Emerging Applications Perspective for Internet of Things

Internet of Things (IoT) is the interconnection of smart physical objects which have Internet connectivity, enabling them to collect data and interact with other devices. It is an evolving technology, which will revolutionize the way we interact with homes, offices, factories, farms, retailing, transportation, logistics etc. In essence, IoT is bound to change how we interact with the physical objects in our surroundings. Technologies like Wireless Sensor Networks and Radio-Frequency Identification are paving the way for the development of this concept. This paper classifies different application domains with a description of some emerging projects and applications in each domain along with the tools which have been used for the same. Next, the challenges and issues in the adoption of IoT have also been conferred.