Abstract: The present disclosure relates to a clustering approach for sensor nodes of a wireless sensor network. This clustering approach, equal distance different members, balances the power burden amongst sensor nodes by deriving an optimal number of sensor nodes at each segment of a length. To this end, the present disclosure describes a linear wireless sensor network wherein the distance between adjacent cluster heads is equal while the number of and distance between sensor nodes in each cluster is different. A power consumption model is derived to aid in the determination of the optimal number of sensor nodes within each cluster. Following evaluation of the cluster approach in comparison with previously described approaches, the present disclosure is observed to improve network longevity and reduce power consumption by deliberately increasing the density of sensor nodes nearest a base station.

Abstract: The present disclosure relates to a clustering approach for sensor nodes of a wireless sensor network. This clustering approach, equal distance different members, balances the power burden amongst sensor nodes by deriving an optimal number of sensor nodes at each segment of a length. To this end, the present disclosure describes a linear wireless sensor network wherein the distance between adjacent cluster heads is equal while the number of and distance between sensor nodes in each cluster is different. A power consumption model is derived to aid in the determination of the optimal number of sensor nodes within each cluster. Following evaluation of the cluster approach in comparison with previously described approaches, the present disclosure is observed to improve network longevity and reduce power consumption by deliberately increasing the density of sensor nodes nearest a base station.

Abstract: The present disclosure relates to a clustering approach for sensor nodes of a wireless sensor network. This clustering approach, equal distance different members, balances the power burden amongst sensor nodes by deriving an optimal number of sensor nodes at each segment of a length. To this end, the present disclosure describes a linear wireless sensor network wherein the distance between adjacent cluster heads is equal while the number of and distance between sensor nodes in each cluster is different. A power consumption model is derived to aid in the determination of the optimal number of sensor nodes within each cluster. Following evaluation of the cluster approach in comparison with previously described approaches, the present disclosure is observed to improve network longevity and reduce power consumption by deliberately increasing the density of sensor nodes nearest a base station.

Abstract: The present disclosure relates to a clustering approach for sensor nodes of a wireless sensor network. This clustering approach, equal distance different members, balances the power burden amongst sensor nodes by deriving an optimal number of sensor nodes at each segment of a length. To this end, the present disclosure describes a linear wireless sensor network wherein the distance between adjacent cluster heads is equal while the number of and distance between sensor nodes in each cluster is different. A power consumption model is derived to aid in the determination of the optimal number of sensor nodes within each cluster. Following evaluation of the cluster approach in comparison with previously described approaches, the present disclosure is observed to improve network longevity and reduce power consumption by deliberately increasing the density of sensor nodes nearest a base station.

Abstract: The present disclosure relates to a clustering approach for sensor nodes of a wireless sensor network. This clustering approach, equal distance different members, balances the power burden amongst sensor nodes by deriving an optimal number of sensor nodes at each segment of a length. To this end, the present disclosure describes a linear wireless sensor network wherein the distance between adjacent cluster heads is equal while the number of and distance between sensor nodes in each cluster is different. A power consumption model is derived to aid in the determination of the optimal number of sensor nodes within each cluster. Following evaluation of the cluster approach in comparison with previously described approaches, the present disclosure is observed to improve network longevity and reduce power consumption by deliberately increasing the density of sensor nodes nearest a base station.

Abstract: The present disclosure relates to a clustering approach for sensor nodes of a wireless sensor network. This clustering approach, equal distance different members, balances the power burden amongst sensor nodes by deriving an optimal number of sensor nodes at each segment of a length. To this end, the present disclosure describes a linear wireless sensor network wherein the distance between adjacent cluster heads is equal while the number of and distance between sensor nodes in each cluster is different. A power consumption model is derived to aid in the determination of the optimal number of sensor nodes within each cluster. Following evaluation of the cluster approach in comparison with previously described approaches, the present disclosure is observed to improve network longevity and reduce power consumption by deliberately increasing the density of sensor nodes nearest a base station.

Abstract: The method of repairing financially infeasible genetic algorithm chromosome encoding activity start times in scheduling problems determines cash availability during a given period, identifies all possible activities' schedules, determines the cash requirements for each schedule, ranks schedules based on the contribution on minimizing the increase in the project duration, schedules all activities of the selected schedule, and determines the impact of the scheduled activities on the project cash flow. Thus, the algorithm introduces effective chromosomes that maximize the utilization of the available funds and minimize project duration.