Collaborative Beamforming (CB) is an essential tool towards achieving long-range transmission in Wireless Sensor Networks (WSNs). In some instances, there may be multiple intended data destinations (sinks) in a WSN. This calls for multi-CB. In comparison to sink-by-sink CB, multi-CB implies improved data rates and decreased co-channel interference; and consequently increased network capacity. In current literature, there is no research in multi-CB particularly in 3-dimension WSNs. In this paper, a novel multi-CB mechanism is brought to the fore. This is from the point of view of a random arrangement of sensor nodes in a 3-dimension manner. It is assumed that all sinks' directions are known at the CB cluster head. Node transmit amplitude and phase are optimized using a Particle Swarm Optimization (PSO) algorithm variant to concurrently achieve balanced multiple narrow beams and minimal radiation in undesired directions. The performance of the proposed scheme is checked against that of a pure multiple beam steering approach (without beam power balancing and minimization of radiation in undesired directions). Moreover, an analysis of beam power, width and steering accuracy is done upon varying the number of collaborating nodes and the collaborating cluster radius. Increasing the count of collaborating nodes yields improved beam precision/accuracy, lower radiation in undesired directions and appreciable stability in beam power performance Increasing the collaborating nodes' cluster radius yields narrow beams, improved beam precision and appreciably lower radiation in undesired directions. The contributions of this work to current literature include: (i) formulation and analysis of a multiple beamforming scheme in the realm of 3-dimension WSNs; (ii) design of a multi-CB scheme taking into account minimization of radiation in undesired directions; (iii) a statistical multi-CB performance analysis upon varying collaborating nodes' cluster radius and collaborating node count.

Null steering is essential in collaborative beamforming (CB) in wireless sensor networks (WSNs) to ensure minimal radiation power and interference in the direction of unintended receivers. Current research in null steering in CB in WSNs is mainly from the perspective of planar arrangements of sensor nodes and sink(s). Furthermore, there is no research dedicated to the formation of multiple wide nulls during CB in 3-dimension WSNs. Wide nulls are ideal in scenarios featuring mobile unintended sink(s). A new multiple and wide null steering scheme applicable to CB in WSNs is presented in this work (from the perspective of a 3-dimensional random arrangement of static sensor nodes). It is assumed that desired nulling directions are implicitly known at a CB cluster head. A particle swarm optimization (PSO) algorithm variant is applied in concurrent node transmit amplitude and phase perturbation with an aim of achieving beam steering alongside multiple and wide null steering. The performance of the proposed null steering scheme is validated against a basic null steering approach (with reference to current literature). Furthermore, a comparative null depth, width, and nulling accuracy analysis are done upon varying the count of collaborating nodes and the collaborating cluster radius. An increase in the number of collaborating nodes is found to increase nulling depth at an exponentially decaying rate. An increase in the collaborating nodes’ cluster radius yields a reduction in null width. The contributions of this work to the existing literature are as follows: (i) the design and investigation of a null steering scheme from the perspective of a 3-dimension random arrangement of sensor nodes; (ii) the design of a concurrent beam steering and multiple wide null steering scheme on the basis of concurrent node transmit amplitude and phase perturbation whilst ensuring null depth uniformity; (iii) a statistical analysis of the impact of a count of collaborating nodes and collaborating cluster radius on nulling performance; (iv) investigation of capacity improvement at unintended receivers upon null steering.

A typical outcome of Collaborative Beamforming (CB) in Wireless Sensor Networks (WSNs) is the presence of relatively high radiation in undesired directions, an aspect attributed to the usual random arrangement of collaborating sensor nodes.High radiation in undesired directions and prominent sidelobes are bound to result in interference in adjacent co-channel networks.Research towards suppression of radiation in undesired directions in CB is active with a number of proposals already in place.Most of the proposals are in the domain/perspective of 2-dimension WSN configuration with a focus on suppressing the highest-leveled (peak) sidelobe only.Commonly, peak sidelobe suppression is achieved through nodes' transmission amplitude perturbation after a conventional phase steering based beamsteering procedure.In this paper, concurrent amplitude and phase perturbation at collaborating nodes has been utilized towards achieving concurrent beamsteering and suppression of radiation in an elaborate set of undesired directions.A variant of the Particle Swarm Optimization (PSO) algorithm has been applied in the node transmit amplitude and phase perturbation process.Selection of radiation suppression directions is done uniformly from the set of all possible undesired radiation directions.A WSN featuring planar node arrangement with the sink at an elevated plane has been used as the analysis platform.The proposed scheme outperforms the peak sidelobe suppression approach in terms of observed radiation in undesired directions and average sidelobe levels.It has also been established that increasing the number of collaborating nodes and/or the number of selected undesired radiation directions in the proposed CB scheme leads to undesired radiation performance improvement although at an exponentially decaying rate.

Collaborative Beamforming (CBF) is an essential tool towards increasing transmission range in Wireless Sensor Networks (WSNs). Owing to the random and complex nature of WSNs, development and use of improved metaheuristic algorithms in CBF is of essence. Particle Swarm Optimization (PSO) algorithm is a good candidate for use in CBF owing to its simplicity and low computation complexity. However, the basic PSO algorithm suffers from premature convergence particularly in highly multimodal functions (typical of CBF). This paper delves into the development and application of an improved Particle Swarm Optimization (PSO) algorithm in CBF. A new fuzzy-logic based confidence and inertia weight parameters adaptation scheme has been developed with an aim of enhancing exploration and exploitation capabilities of the PSO algorithm. Normalized particle quality and iteration count have been used as the inputs to the designed fuzzy-logic inference system. The fuzzy logic based parameters adaptation scheme has been implemented in the form of a lookup table to minimize "on-line" computation complexity. Furthermore, a particle culling/ re-initialization procedure is utilized at half the number of maximum iterations to enhance overall swarm diversity. The modified PSO algorithm has been christened Culled Fuzzy Adaptive Particle Swarm Optimization (CFAPSO) algorithm. The developed CFAPSO algorithm is noted to outperform other metaheuristic algorithms in a statistical performance analysis procedure (on the basis of a set of standard unimodal and multimodal functions). Upon application to CBF, the CFAPSO algorithm is found to generate a beamsteering outcome statistically identical to that of conventional beamsteering.

Collaborative beamforming (CBF) has been proposed as a viable tool towards increasing node transmission range in Wireless Sensor Networks (WSNs). This is highly essential in scenarios featuring highly elevated airborne sinks (Unmanned Aerial Vehicles (UAVs)) where multi-hop transmission is inconceivable. CBF yields radiation in unintended directions (sidelobes). A high sidelobe is bound to lead to interference at an UAV in the sidelobe’s direction (taking into consideration a case of multiple and mobile UAV sinks). A direct consequence of increased interference is reduction in communication capacity at the affected UAV. This paper presents a generalized sidelobe minimization scheme using Gravitational Search Algorithm (GSA). The effect of sidelobe minimization on the communication capacity at unintended UAVs is analyzed. Appreciable capacity increase is observed in comparison to a scheme featuring peak sidelobe minimization only.

Switched beamforming using electronic phase shifters is commonplace. Digital switched beamformers offer a premise of better performance than electronic phase shift switched beamformers. It is also worth noting that current unknown signal Direction of Arrival (DoA) estimation methods (commonly MUltiple SIgnal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)) are generally computationally intensive. In this paper, signal DoA estimation and digital switched beamforming using aptly designed Artificial Neural Network (ANN) classifiers are looked into. Initially, signals detected at a rectangular receiving array are mapped onto a DoA through an ANN classifier. A second ANN classifier maps the selected DoA onto an optimal set of beamforming weights leading to an optimal switched beamforming reception pattern. The ANN classifiers’ performance in DoA estimation and beamforming is tested over a variety of trials, yielding good results. The designed ANN beamformer premises to yield high-speed and accurate switched beamforming performance, most notably in large array systems. The ANN DoA estimator/beamformer can be easily adapted to nonuniform arrays wherein closed form DoA estimation/beamforming solutions are impractical. MATLAB software environment has been used as the main analysis tool.

Studies reveal that there are many benefits resulting from using logistics outsourcing strategy in the oil and gas industry and in the same way, challenges are faced by both oil and gas companies and logistics service providers in executing business contracts. The most outstanding benefit is that, companies outsource their logistics activities to restructure their distribution networks and achieve competitive advantage. Solutions are pursued to create an environment for the oil and gas industry and the third party logistics services providers to achieve a common goal. Importance is pegged on outsourcing full or part of logistics activities to logistics service providers in order to attain competitive advantage. Logistics outsourcing strategy plays an important role in the business especially in minimizing operating costs and spreading risk between the parties engaged in business contracts. Therefore it is necessary for company managers to check their strategies to make sure that logistics activities are in place. This study was set to examine the relationship between logistics outsourcing and oil and gas project performance in Kenya. Literature was reviewed with emphasis on the relationship between the variables, theoretical literature and empirical literature. The unit of analysis was all managing directors of the 71 registered oil and gas players who gave the information on Oil and Gas Project performance and logistics outsourcing. Data was generated by means of questionnaires to oil and gas players on the variables. Responses were statistically analyzed using descriptive statistics, product moment correlation and regression analysis. Data was presented using charts, tables and figures. The study found that there was positive correlation coefficient between Oil and Gas projects and Transportation Outsourcing. The study found weak positive correlation between Oil and Gas projects and Inventory Management Outsourcing. Key Words: Transport, Inventory Management, Outsourcing, Project Performance

Digital beamforming involves use of computation algorithms to generate array weights that optimize antenna array radiation patterns in a wireless communication link. Such algorithms are designed and implemented based on particular beamforming approaches.  In this paper, adaptive beamforming using a beam/ null steering approach in a rectangular antenna array system is studied. The beamforming approach is implemented using Particle Swarm Optimization (PSO) algorithm and Simulated Annealing (SA) algorithm. The weights of the beam/ null steering beamformer are synthesized to produce maximum radiation in desired directions and minimal radiation in undesired directions. The resultant beam shapes and their characteristics are used as measures of success. The PSO algorithm is found to be highly effective in implementing beam/ null steering compared to the SA algorithm.