Maximizing Coverage Quality Using Minimum Number of Nodes in Clustered Directional Sensor Networks

Abstract

The invention of directional sensor nodes has given birth of a special kind of network namely Directional Sensor Network (DSN), which provides better network lifetime and sensing coverage compared to its omni-directional counterpart. These two cutting-edge features help DSNs attracting interests of research and industrial communities, particularly for the areas of high quality sensing in Smart City applications including healthcare, infrastructure security, traffic and access monitoring, etc. In this thesis, we focus on two fundamental reasearch topics of DSNs - area coverage and target coverage. The area coverage problem in Directional Sensor Networks (DSNs) presents great research challenges including minimization of number of active sensors and overlapping sensing coverage area among those, determination of their active sensing directions in an energy-efficient way, etc. Existing solutions permit to execute coverage enhancement algorithms at each individual sensor nodes, leading to high communication and computation overheads, loss of energy and reduced sensing coverage. In this thesis, we first formulate the problem of maximizing area coverage with minimum number of active nodes as a mixed-integer linear programming (MILP) optimization problem for a clustered DSN. Due to its NP-completeness, we then develop a greedy alternate solution, namely α-overlapping area coverage (α-OAC). In α-OAC, each cluster head (CH) takes the responsibility of determining the active member nodes and their sensing directions; where, each sensing node is allowed to have at most α% coverage overlapping with its neighbors. The α-OAC CHs activate a sensor node if and only if the later has sufficient residual energy and send other member nodes to the sleep state. The proposed α-OAC system is distributed and scalable since it requires single-hop neighborhood informationonly. Results from extensive simulations, done in Network Simulator version 3 (ns-3), reveal that the α-OAC system outperforms state-of-the-art works in terms of area coverage, network lifetime and operation overhead. Conventional researches on target coverage in Directional Sensor Networks (DSNs) mainly focus to increase the network lifetime, overlooking the coverage quality of targets; especially, they don’t consider the targets that have heterogeneous coverage requirements. Increasing sensing quality is of utmost importance to ensure comfort living in Smart Cities. In this dissertation, we have designed a generalized framework, namely MQMS-DSN (Maximizing coverage Quality with Minimum number of Sensors in DSN), that has the ability to maximize the target coverage quality or the network lifetime or to make an efficient tradeoff between the two following an application demand. Using a probabilistic model for measuring the sensing coverage quality, we have developed optimal, suboptimal and greedy solutions for MQMS problem. Empirical evaluations of the proposed MQMS systems have been carried out in ns-3. The results show the effectiveness of the proposed systems compared to state-of-the-art-works in terms of sensing quality and network lifetime.