Nabil Sabor

Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks

Energy hole problem is a critical issue for data gathering in wireless sensor networks. Sensors near the static sink act as relays for far sensors and thus will deplete their energy very quickly, resulting energy holes in the sensor field. Exploiting the mobility of a sink has been widely accepted as an efficient way to alleviate this problem. However, determining an optimal moving trajectory for a mobile sink is a non-deterministic polynomial-time hard problem. Thus, this paper proposed a mobile sink-based adaptive immune energy-efficient clustering protocol (MSIEEP) to alleviate the energy holes. A MSIEEP uses the adaptive immune algorithm (AIA) to guide the mobile sink-based on minimizing the total dissipated energy in communication and overhead control packets. Moreover, AIA is used to find the optimum number of cluster heads (CHs) to improve the lifetime and stability period of the network. The performance of MSIEEP is compared with the previously published protocols; namely, low-energy adaptive clustering hierarchy (LEACH), genetic algorithm-based LEACH, amend LEACH, rendezvous, and mobile sink improved energy-efficient PEGASIS-based routing protocol using MATLAB. Simulation results show that MSIEEP is more reliable and energy efficient as compared with other protocols. Furthermore, it improves the lifetime, the stability, and the instability periods over the previous protocols, because it always selects CHs from high-energy nodes. Moreover, the mobile sink increases the ability of the proposed protocol to deliver packets to the destination.

A centralized immune-Voronoi deployment algorithm for coverage maximization and energy conservation in mobile wireless sensor networks

A Centralized Immune-Voronoi deployment Algorithm (CIVA) is proposed.CIVA considers the binary and the probabilistic model for enhancing the coverage.CIVA adjusts the positions, the sensing ranges and the radios of MSNs in MWSN.CIVA provides a better trade-off between the coverage and the energy consumption.Simulation experiments were conducted in MATLAB correctly. Saving energy is a most important challenge in Mobile Wireless Sensor Networks (MWSNs) to extend the lifetime, and optimal coverage is the key to it. Therefore, this paper proposes a Centralized Immune-Voronoi deployment Algorithm (CIVA) to maximize the coverage based on both binary and probabilistic models. CIVA utilizes the multi-objective immune algorithm that uses the Voronoi diagram properties to provide a better trade-off between the coverage and the energy consumption. The CIVA algorithm consists from two phases to improve the lifetime and the coverage of MWSN. In the first phase, CIVA controls the positions and the sensing ranges of Mobile Sensor Nodes (MSNs) based on maximizing the coverage and minimizing the dissipated energy in mobility and sensing. While the second phase of CIVA adjusts the radio (sleep/active) of MSNs to minimize the number of active sensors based on minimizing the consumption energy in sensing and redundant coverage and preserving the coverage at high level. The performance of the CIVA is compared with the previous algorithms using Matlab simulation for different network configurations with and without obstacles. Simulation results show that the CIVA algorithm outperforms the previous algorithms in terms of the coverage and the dissipated energy for different networks configurations.

An Unequal Multi-hop Balanced Immune Clustering protocol for wireless sensor networks

Display Omitted An Unequal Multi-hop Balanced Immune Clustering protocol (UMBIC) is proposed.UMBIC solves the hot spot problem and improves the lifetime of networks.UMBIC adjusts the intra-cluster and inter-cluster energy dissipation of clusters.UMBIC utilizes the multi-objective immune algorithm to finds the optimum clusters.Simulation experiments were conducted in MATLAB correctly. In multi-hop routing, cluster heads near the base station act as relays for far cluster heads and thus will deplete their energy very quickly. Thus, hot spots in the sensor field result. This paper introduces a new clustering algorithm named an Unequal Multi-hop Balanced Immune Clustering protocol (UMBIC) to solve the hot spot problem and improve the lifetime of small and large scale/homogeneous and heterogeneous wireless sensor networks with different densities. UMBIC protocol utilizes the Unequal Clustering Mechanism (UCM) and the Multi-Objective Immune Algorithm (MOIA) to adjust the intra-cluster and inter-cluster energy consumption. The UCM is used to partition the network into clusters of unequal size based on distance with reference to base station and residual energy. While the MOIA constructs an optimum clusters and a routing tree among them based on covering the entire sensor field, ensuring the connectivity among nodes and minimizing the communication cost of all nodes. The UMBIC protocol rotates the role of cluster heads among the nodes only if the residual energy of one of the current cluster heads less than the energy threshold, as a result the time computational and overheads are saved. Simulation results show that, compared with other protocols, the UMBIC protocol can effectively improve the network lifetime, solve the hot spot problem and balance the energy consumption among all nodes in the network. Moreover, it has less overheads and computational complexity.

A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions

Introducing mobility to Wireless Sensor Networks (WSNs) puts new challenges particularly in designing of routing protocols. Mobility can be applied to the sensor nodes and/or the sink node in the network. Many routing protocols have been developed to support the mobility of WSNs. These protocols are divided depending on the routing structure into hierarchical-based, flat-based, and location-based routing protocols. However, the hierarchical-based routing protocols outperform the other routing types in saving energy, scalability, and extending lifetime of Mobile WSNs (MWSNs). Selecting an appropriate hierarchical routing protocol for specific applications is an important and difficult task. Therefore, this paper focuses on reviewing some of the recently hierarchical-based routing protocols that are developed in the last five years for MWSNs. This survey divides the hierarchical-based routing protocols into two broad groups, namely, classical-based and optimized-based routing protocols. Also, we present a detailed classification of the reviewed protocols according to the routing approach, control manner, mobile element, mobility pattern, network architecture, clustering attributes, protocol operation, path establishment, communication paradigm, energy model, protocol objectives, and applications. Moreover, a comparison between the reviewed protocols is investigated in this survey depending on delay, network size, energy-efficiency, and scalability while mentioning the advantages and drawbacks of each protocol. Finally, we summarize and conclude the paper with future directions.

ON AUTONOMOUS AND NONAUTONOMOUS MODIFIED HYPERCHAOTIC COMPLEX LÜ SYSTEMS

In this paper autonomous and nonautonomous modified hyperchaotic complex Lu systems are proposed. Our systems have been generated by using state feedback and complex periodic forcing. The basic properties of these systems are studied. Parameters range for hyperchaotic attractors to exist are calculated. These systems have very rich dynamics in a wide range of parameters. The analytical results are tested numerically and excellent agreement is found. A circuit diagram is designed for one of these hyperchaotic complex systems and simulated using Matlab/Simulink to verify the hyperchaotic behavior.

Rearrangement of mobile wireless sensor nodes for coverage maximization based on immune node deployment algorithm

Graphical abstractDisplay Omitted A new deployment approach for WSNs based on the multi-objective immune algorithm is proposed.The approach rearranges sensors to remit the coverage holes and improves the network coverage.The approach is energy efficient and ensures the connectivity by limiting the mobility cost of sensors.Simulation experiments were completed in MATLAB correctly.The approach has many advantages comparing with other algorithms. One of the primary objectives of Wireless Sensor Network (WSN) is to provide full coverage of a sensing field as long as possible. The deployment strategy of sensor nodes in the sensor field is the most critical factor related to the network coverage. However, the traditional deployment methods can cause coverage holes in the sensing field. Therefore, this paper proposes a new deployment method based on Multi-objective Immune Algorithm (MIA) and binary sensing model to alleviate these coverage holes. MIA is adopted here to maximize the coverage area of WSN by rearranging the mobile sensors based on limiting their mobility within their communication range to preserve the connectivity among them. The performance of the proposed algorithm is compared with the previous algorithms using Matlab simulation for different network environments with and without obstacles. Simulation results show that the proposed algorithm improves the coverage area and the mobility cost of WSN.

Coverage maximization in mobile Wireless Sensor Networks utilizing immune node deployment algorithm

A Wireless Sensor Network (WSN) consists of spatially distributed autonomous sensors with sensing, computation and wireless communication capabilities. Each sensor generally has the task to monitor, measure ambient conditions, and disseminate the collected data towards a base station. One of the key points in the design stage of a WSN that is related to the sensing attribute is the coverage of the sensing field. The coverage issue in WSNs depends on many factors, such as the network topology, sensor sensing model, and the most important one is the deployment strategy. The sensor nodes can be deployed either deterministically or randomly. Random deployment of the sensor nodes can cause coverage holes formulation; therefore, in most cases, random deployment is not guaranteed to be efficient for achieving the required coverage. In this case, the mobility feature of the nodes can be utilized in order to maximize the coverage. This is Non-deterministic Polynomial-time hard (NP-hard) problem. So in this paper, the Immune Algorithm (IA) is used to relocate the mobile sensor nodes after the initial configuration to maximize the coverage area with the moving dissipated energy minimized. The performance of the proposed algorithm is compared with the previous algorithms using Matlab simulation. Simulation results show that the proposed algorithm improves the network coverage and the redundant covered area with minimum moving consumption energy.

Utilisation of multi-objective immune deployment algorithm for coverage area maximisation with limit mobility in wireless sensors networks

Coverage is one of the most important performance metrics for wireless sensor network (WSN) since it reflects how well a sensor field is monitored. The coverage issue in WSNs depends on many factors, such as the network topology, sensor sensing model and the most important one is the deployment strategy. Random deployment of the sensor nodes can cause coverage holes formulation. This problem is non-deterministic polynomial-time hard problem. So in this study, a new centralised deployment algorithm based on the immune optimisation algorithm is proposed to relocate the mobile nodes after the initial configuration to maximise the coverage area. Moreover, the proposed algorithm limits the moving distance of the mobile nodes to reduce the dissipation energy in mobility and to ensure the connectivity among the sensor nodes. The performance of the proposed algorithm is compared with the previous algorithms using Matlab simulation. Simulation results clear that the proposed algorithm based on binary and probabilistic sensing models improves the network coverage and the redundant covered area with minimum moving consumption energy. Furthermore, the simulation results show that the proposed algorithm also works when obstacles appear in the sensing field.

ARBIC: An Adjustable Range Based Immune hierarchy Clustering protocol supporting mobility of Wireless Sensor Networks

Abstract Introducing the mobility to Wireless Sensor Networks (WSNs) puts new challenges in designing an energy-efficient routing. Improving the network lifetime and the packet delivered rate are the most important issues in designing of the Mobile Wireless Sensor Networks (MWSNs). MWSN is more difficult to deal with than its stationary counterpart because it does not have a fixed topology. This increases the complexity of routing due to the frequent link breaks between clusters and their members. Various clustering protocols are developed to support mobility of the nodes in the WSNs. However, these protocols suffer from some limitations in connectivity, energy-efficient, fault tolerance, load balancing and mobility adaption because they organize the network into fixed size clusters and select the heads of these clusters randomly. Thus, this paper proposes an Adjustable Range-Based Immune hierarchy Clustering protocol (ARBIC) with mobility supporting to deliver the sensory data of the MWSN to the base station in an efficient way for a long-time. The operation of ARBIC protocol depends on organizing the network into optimum clusters and adjusting the size of these clusters based on the speed of the mobile sensor nodes to preserve the cluster connectivity. ARBIC protocol utilizes the immune optimization algorithm to determine the best positions of the clusters’ heads that optimize the trade-off among the mobility factor, energy consumption, connectivity, residual energy and link connection time. In order to save the overhead packets and the computational time, the ARBIC protocol runs the clustering process if and only if the residual energy of any cluster head is less than a predefined energy threshold. Moreover, it performs a fault tolerance mechanism after sending each frame to reduce the packets drop rate by maintaining the stability of links between the clusters’ heads and their member nodes. Mathematical analyses are established to analyze the computational and overhead complexities of the ARBIC protocol. Simulation results show that, compared with other protocols, the ARBIC protocol can effectively improve the packet delivery ratio while simultaneously offering lower energy consumption and delay by using sensor nodes with adjustable transmission ranges.

Design of Immune Algorithm based two-dimensional recursive digital filters using multi-level Orthogonal Arrays

Taguchi Immune Algorithm (TIA) is based on both features of the biological immune system and the Taguchi method which increases the ability of the Immune Algorithm (IA) to find the global optimal solution in a nonlinear space. In the TIA, the clonal proliferation within hypermutation for several antibody diversifications and the recombination by using the Taguchi method for the local search are integrated to improve the capabilities of exploration and exploitation. Two major tools are used in the Taguchi method; namely the Orthogonal Arrays (OAs) and the Signal to Noise Ratio (SNR). The effect of selecting the number of levels adopted in the construction of OAs on TIA is not studied before. So, this paper addresses the problem increasing the convergence speed of immune algorithm based two-dimensional recursive digital filters design process by adopting two, three and four levels OAs. For seek of comparison, the same computational experiments adopted in [1] are considered. Numerical results show that increasing the number of OA levels yields to faster convergence and better antibody genes selection in order to achieve the potential recombination, and consequently enhance the design process.