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.

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.

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.

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.

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.

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.

Fiber reinforced plastic (FRP) jacketing has emerged as an effective way to retrofit concrete columns in recent years. The existing experimental work on FRP confined concrete column is mainly concentrated on concrete columns under concentric loading. Therefore, this research involves the study of nonlinear finite element analysis of FRP confined square and rectangular R.C. columns under both axial compressive and lateral loads. Lateral load was taken as a ratio of the axial compressive loads "H/ Pc". The effects of the FRP layer number, concrete compressive strength, percentage of main steel, rectangularity of columns and various ratios of lateral loads to centric load on FRP confined concrete columns were investigated theoretically. The results of this work were compared with previous work [1] considered the behavior of square and rectangular columns confined with CFRP under centric loads. All studied columns were modeled using the nonlinear finite element analysis. The main variables studied were the cross-section shape of the column “R”, concrete grade “C”, the percentage of area of the main longitudinal steel “As%” and the number of layers of CFRP sheet “NL”. The FEM results indicated that R.C. columns externally wrapped with CFRP sheet showed a significant improvement in both capacity and ductility.

Fiber reinforced plastic (FRP) jacketing has emerged as an effective way to retrofit concrete columns in recent years. The existing experimental work on FRP confined concrete column is mainly concentrated on concrete columns under concentric loading. Therefore, this research involves the study of nonlinear finite element analysis of FRP confined square and rectangular R.C. columns under both axial compressive and lateral loads. Lateral load was taken as a ratio of the axial compressive loads "H/ Pc". The effects of the FRP layer number, concrete compressive strength, percentage of main steel, rectangularity of columns and various ratios of lateral loads to centric load on FRP confined concrete columns were investigated theoretically. The results of this work were compared with previous work [1] considered the behavior of square and rectangular columns confined with CFRP under centric loads. All studied columns were modeled using the nonlinear finite element analysis. The main variables studied were the cross-section shape of the column “R”, concrete grade “C”, the percentage of area of the main longitudinal steel “As%” and the number of layers of CFRP sheet “NL”. The FEM results indicated that R.C. columns externally wrapped with CFRP sheet showed a significant improvement in both capacity and ductility.

Fiber reinforced plastic (FRP) jacketing has emerged as an effective way to retrofit concrete columns in recent years. The existing experimental work on FRP confined concrete column is mainly concentrated on concrete columns under concentric loading. Therefore, this research involves the study of nonlinear finite element analysis of FRP confined square and rectangular R.C. columns under both axial compressive and lateral loads. Lateral load was taken as a ratio of the axial compressive loads "H/ Pc". The effects of the FRP layer number, concrete compressive strength, percentage of main steel, rectangularity of columns and various ratios of lateral loads to centric load on FRP confined concrete columns were investigated theoretically. The results of this work were compared with previous work [1] considered the behavior of square and rectangular columns confined with CFRP under centric loads. All studied columns were modeled using the nonlinear finite element analysis. The main variables studied were the cross-section shape of the column “R”, concrete grade “C”, the percentage of area of the main longitudinal steel “As%” and the number of layers of CFRP sheet “NL”. The FEM results indicated that R.C. columns externally wrapped with CFRP sheet showed a significant improvement in both capacity and ductility.

Fiber reinforced plastic (FRP) jacketing has emerged as an effective way to retrofit concrete columns in recent years. The existing experimental work on FRP confined concrete column is mainly concentrated on concrete columns under concentric loading. Therefore, this research involves the study of nonlinear finite element analysis of FRP confined square and rectangular R.C. columns under both axial compressive and lateral loads. Lateral load was taken as a ratio of the axial compressive loads "H/ Pc". The effects of the FRP layer number, concrete compressive strength, percentage of main steel, rectangularity of columns and various ratios of lateral loads to centric load on FRP confined concrete columns were investigated theoretically. The results of this work were compared with previous work [1] considered the behavior of square and rectangular columns confined with CFRP under centric loads. All studied columns were modeled using the nonlinear finite element analysis. The main variables studied were the cross-section shape of the column "R", concrete grade "C", the percentage of area of the main longitudinal steel "As%" and the number of layers of CFRP sheet "NL". The FEM results indicated that R.C. columns externally wrapped with CFRP sheet showed a significant improvement in both capacity and ductility.