Citizens' Assessment on Programs for Education of the Local Government Unit of Banga, Aklan

Citizen Satisfaction Index System (CSIS) was used to assess the delivery of support to education initiatives in the municipality of Banga, Aklan, Philippines. The samples were determined using multiple application of stratified random sampling approach. In accordance with the Philippine Statistical Authority's Data on Census Population and Housing for 2015, barangays having a bigger share of the population contributed more respondents to the 150 targeted participants. Following the inclusion criteria, the probability respondents were chosen using the Kish Grid. Pre-numbered questionnaires were distributed, with odd numbers targeting male responders and even numbers targeting females. The following criteria were used to evaluate the respondents' assessments: awareness, availment of the program/service, satisfaction, and need for action. Furthermore, interviews were conducted to better understand and investigate the respondents' thoughts, behavior, and perspectives. The reasons for their reaction were also obtained. The data was provided in percentage as well as frequency distributions. The study inferred that awareness on alternative learning system and/or special education program should be improved since it is the only program that attained low rating for awareness. Most of the residents were not able to avail education programs because they have no household member who attends school. Majority of the respondents who availed the services were satisfied. However, scholarship and other assistance programs to students may be enhanced to improve service delivery. It is highly recommended by the citizens to improve accessibility to scholarship programs and other forms of financial assistance to students.

Energy Efficient Adaptive Data Aggregation in Cluster Based Wireless Sensor Network

The world in future will consist of smart environments that would primarily rely on sensory data from real world. Therefore, requirement of large scale Wireless Sensor Networks (WSNs) is inevitable in future. WSNs are made up of spatially distributed wireless sensor nodes that have limited resources, among which the power resource is the most crucial one. It has been seen previously that the sensor nodes consume around hundred to thousand times more energy in transmission of data than in execution of instructions. This has made the collection of data from large scale WSNs with minimal energy consumption as a big challenge. Various techniques have been adopted to reduce the consumption of energy in data collection. Among these, one signi cant means of energy conservation is the use of small sized data packets. In WSNs small sized data packets have shown to be more energy e cient than large and variable sized packets. However, this limitation in packet sizes require compression of data during its aggregation on the intermediate nodes in between the data collection points and the destination. In large-scale dynamic cluster based WSNs, the clusters are not created uniformly causing highly variable number of nodes in di erent clus- ters of the same network. Problem arises in large sized clusters where large amount of data is required to be transmitted within small packets. Predetermined compres- sion techniques require di erent sizes of packets for di erent sized clusters to maintain same level of losses in data. On the other hand, for small sized clusters, predetermined compression techniques may unnecessarily compress the data and incur losses. There- fore an aggregation technique is required that can control the compression of variable amount of data according to the space available in data packet while causing minimal data distortion. This thesis proposes an adaptive data aggregation algorithm that can adjust the com- pression level of data on a cluster head according to given payload size in real time while ensuring minimal distortion in the data. Thus wide range of data can optimally be adjusted in packets whose sizes have been regulated based on the channel conditions and transmission energy utilization. To the best of our knowledge no other work has been done in this domain where an adaptive aggregation of data is performed considering together the size of cluster, size of data packet available at cluster head and the spatial correlation among the data. To improve the performance of the proposed aggregation algorithm, uniformity in clus- ter sizes were required. Existing uniform clustering mechanisms have shown extra net- work energy consumption when applied on dynamic clustering protocols. Therefore, a uniform clustering technique is proposed in this thesis that can reduce the variability of cluster sizes in dynamic cluster based networks without consuming additional energy of the network. During the simulation of dynamic cluster based WSNs it was also ob- served that the broadcasting of control packets for cluster setup consumes considerable amount of network energy. Therefore, to reduce this overhead energy consumption, a mechanism is proposed that reduces the amount of broadcast packets for cluster setup. Altogether, in this thesis, a set of energy consumption issues are addressed that arise due to redundant data and control transmissions in dynamic cluster based WSNs. The main objective is to reduce the size of data that is transmitted in the network with minimal losses and to reduce the amount of control information during the cluster setup. The net result of the proposed set of solutions is the network energy conservation, network lifetime enhancement, optimal utilization of limited sized payload and load balancing in dynamic cluster based wireless sensor networks.