Huma Javed

Analytical network process based optimum cluster head selection in wireless sensor network

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process.

Grid-Based Hybrid Network Deployment Approach for Energy Efficient Wireless Sensor Networks

Wireless sensor networks (WSN) empower applications for critical decision-making through collaborative computing, communications, and distributed sensing. However, they face several challenges due to their peculiar use in a wide variety of applications. One of the inherent challenges with any battery operated sensor is the efficient consumption of energy and its effect on network lifetime. In this paper, we introduce a novel grid-based hybrid network deployment (GHND) framework which ensures energy efficiency and load balancing in wireless sensor networks. This research is particularly focused on the merge and split technique to achieve even distribution of sensor nodes across the grid. Low density neighboring zones are merged together whereas high density zones are strategically split to achieve optimum balance. Extensive simulations reveal that the proposed method outperforms state-of-the-art techniques in terms of load balancing, network lifetime, and total energy consumption.

Energy Efficient Hierarchical Clustering Approaches in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSN) are one of the significant technologies due to their diverse applications such as health care monitoring, smart phones, military, disaster management, and other surveillance systems. Sensor nodes are usually deployed in large number that work independently in unattended harsh environments. Due to constraint resources, typically the scarce battery power, these wireless nodes are grouped into clusters for energy efficient communication. In clustering hierarchical schemes have achieved great interest for minimizing energy consumption. Hierarchical schemes are generally categorized as cluster-based and grid-based approaches. In cluster-based approaches, nodes are grouped into clusters, where a resourceful sensor node is nominated as a cluster head (CH) while in grid-based approach the network is divided into confined virtual grids usually performed by the base station. This paper highlights and discusses the design challenges for cluster-based schemes, the important cluster formation parameters, and classification of hierarchical clustering protocols. Moreover, existing cluster-based and grid-based techniques are evaluated by considering certain parameters to help users in selecting appropriate technique. Furthermore, a detailed summary of these protocols is presented with their advantages, disadvantages, and applicability in particular cases.

Multicriteria Based Next Forwarder Selection for Data Dissemination in Vehicular Ad Hoc Networks Using Analytical Network Process

Vehicular ad hoc network (VANET) is a wireless emerging technology that aims to provide safety and communication services to drivers and passengers. In VANETs, vehicles communicate with other vehicles directly or through road side units (RSU) for sharing traffic information. The data dissemination in VANETs is a challenging issue as the vehicles have to share safety critical information in real time. The data distribution is usually done using broadcast method resulting in inefficient use of network resources. Therefore, to avoid the broadcast storm and efficiently use network resources, next forwarder vehicle (NFV) is selected to forward data to nearby vehicles. The NFV selection is based on certain parameters like direction, distance, and position of vehicles, which makes it a multicriteria decision problem. In this paper, analytical network process (ANP) is used as a multicriteria decision tool to select the optimal vehicle as NFV. The stability of alternatives (candidate vehicles for NFV selection) ranking is checked using sensitivity analysis for different scenarios. Mathematical formulation shows that ANP method is applicable for NFV selection in VANETs. Simulation results show that the proposed scheme outperforms other state-of-the-art data dissemination schemes in terms of reachability, latency, collisions, and number of transmitted and duplicate data packets.

CoXoH: Low cost energy efficient data compression for wireless sensor nodes using data encoding

The limited resources of Wireless Sensor Networks such as battery power, storage and processing power needs to be utilized very efficiently to prolong network life. WSN operates on battery power which cannot be replaced easily. Since data transmission consumes most amount of energy, therefore data needs to be compressed before transmission. Data compression will reduce the size of data to be processed and transmitted for saving motes limited resources. This paper proposes a new low cost, lossless, energy efficient algorithm called CoXoH (Combined XOR and Huffman) using number encoding for data compression which guarantees the compression of at-least 50%. However the simulation results show that up-to 98% compression can also be achieved which almost double the battery life. CoXoH is the combination of two operations XOR and Huffman Algorithm. XOR operation will reduce the data to 50% followed by Huffman compression which will further compress the data. The average compression ratio is from 70% to 90%.

Multi-criteria based zone head selection in Internet of Things based wireless sensor networks

Abstract The past few years have seen dramatic development and a great interest in efficient service delivery and better resource utilization in the Internet of Things (IoT) based constrained Wireless Sensor Network (WSN). The IoT is mainly dependent on optimal deployment of energy aware WSN and efficient communication architecture for data transfer among heterogeneous devices. In addition, energy efficient clustering techniques for WSN node deployment and routing have achieved great involvement for prolonging network lifetime. In clustering technique, where the network is partitioned into different segments (clusters or zones) and proper attention must be given to the cluster head (CH) selection procedure for maximizing node reachability inside the cluster and efficient communication to the base station. In this paper, we have proposed multi-criteria based cluster head/zone head selection scheme in Internet of Things based WSN by considering distinct parameters affecting node energy and network lifetime. These parameters; energy level, distance from neighboring nodes, distance from center of the zone, number of times a node has been zone head and whether a node is merged or not, have direct impact on overall performance of WSN. The relative impact of each parameter in CH/ZH selection is computed using the Analytical Network Process (ANP) which is widely used multi-criteria decision tool. Simulation results of the proposed scheme show relatively better performance than existing energy efficient clustering techniques. The obtained results have been analyzed by varying the number of parameters in ZH selection and their impact on network stability and lifetime.

Multicriteria-Based Location Privacy Preservation in Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) are the preferable choice for Intelligent Transportation Systems (ITS) because of its prevailing significance in both safety and nonsafety applications. Information dissemination in a multihop fashion along with privacy preservation of source node is a serious but challenging issue. We have used the idea of the phantom node as the next forwarder for data dissemination. The phantom node (vehicle) hides the identity of actual source node thus preserving the location privacy. The selection of the phantom node among the set of alternatives’ candidate vehicles is considered as a multicriteria-based problem. The phantom node selection problem is solved by using an analytical network process (ANP) by considering different traffic scenarios. The selection is based on different parameters which are distance, speed, trust, acceleration, and direction. The best alternative (target phantom vehicle) is selected through an ANP where all the alternatives are ranked from best to worst. The vehicle having maximum weight is considered to be the best choice as a phantom node. In order to check the stability of the alternatives’ ranking, sensitivity analysis is performed by taking into account different traffic scenarios and interest level of candidate vehicles.

An immunology inspired flow control attack detection using negative selection with R -contiguous bit matching for wireless sensor networks

Wireless sensor networks (WSNs) due to their deployment in open and unprotected environments become suspected to attacks. Most of the resource exhaustion occurs as a result of attacking the data flow control thus creating challenges for the security of WSNs. An Anomaly Detection System (ADS) framework inspired from the Human Immune System is implemented in this paper for detecting Sybil attacks in WSNs. This paper implemented an improved, decentralized, and customized version of the Negative Selection Algorithm (NSA) for data flow anomaly detection with learning capability. The use of R-contiguous bit matching, which is a light-weighted bit matching technique, has reduced holes in the detection coverage. This paper compares the Sybil attack detection performance with three algorithms in terms of false negative, false positive, and detection rates. The higher detection, and lower false positive and false negative rates of the implemented technique due to the R-contiguous bit matching technique used in NSA improve the performance of the proposed framework. The work has been tested in Omnet++ against Sybil attacks for WSNs.

Native process migration in wireless sensor networks

This paper presents a novel architecture for native process migration (PM) in wireless sensor networks (WSNs) without the use of virtual execution environment. Resources in WSN are scarce; therefore creating virtual execution environment puts extra burden on already stringent resources. In addition, the proposed architecture is migrating with complete process instead of code only which also saves resources. The proposed architecture makes process migration decisions by continuously monitoring resources, such as remaining battery life and free memory space on a node. The architecture is suitable for networks with fewer expensive sensor nodes as it allows for better utilization of network resources. Transferring a live executing process from one node to another to meet processing demands dynamically improves fault tolerance, resource utilization, and network management in WSN. The architecture has been successfully tested and implemented on both COOJA simulator and a test bed of TelosB motes.