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Localization is an important research issue inWireless Sensor Networks (WSNs). Though Global Positioning System (GPS) can be used to locate the position of the sensors, unfortunately it is limited to outdoor applications and is costly and power consuming. In order to find location of sensor nodes without help of GPS, collaboration among nodes is highly essential so that localization can be accomplished efficiently. In this paper, novel localization algorithms are proposed to find out possible location information of the normal nodes in a collaborative manner for an outdoor environment with help of few beacons and anchor nodes. In our localization scheme, at most three beacon nodes should be collaborated to find out the accurate location information of any normal node. Besides, analytical methods are designed to calculate and reduce the localization error using probability distribution function. Performance evaluation of our algorithm shows that there is a tradeoff between deployed number of beacon nodes and localization error, and average localization time of the network can be increased with increase in the number of normal nodes deployed over a region. [ABSTRACT FROM AUTHOR]

An ad-hoc wireless network has multihop architecture and is more mobile than single-hop network architecture in the real world. But the ad-hoc wireless network has some challenge with respect to mobility, real-time communication, routing path, maintenance, spatial reuse, bandwidth management, and packets broadcast overhead. This paper investigates two important issues tn ensure more stable path routing and less re-clustering to improve the system performance. Novel Linked Stability-Based Clustering (LSC) and Linked Stability-Based Routing (LSR) algorithms, using fuzzy set theory, are proposed. The LSC algorithm guarantees the stability of the cluster to reduce the probability of re-clustering because the cluster-head is not easily replaced. The LSC algorithm proposed in this paper reduces the easy re-clustering problem of the HCC algorithm by considering not only connectivity but also the link's signal strength between the mobile nodes obtained from a fuzzy set in cluster-head determination. The membership function of the LSC algorithm, based on the link's signal strength, predicts a more stable link routing path using fuzzy set theory. Simulation results show that the proposed algorithm ensures the stability of the cluster and avoids unnecessary re-clustering; for example, the LSC algorithm occurs less frequently than LID and HC algorithms. Similarly, the LSR routing algorithm that uses fuzzy sets and membership functions is based on the mean signal strength and the relative movement between nodes, to obtain the lifetime of each connection and the path lifetime, from fuzzy inferences and fuzzy rules for reference. The LSR algorithm provides more reliable path lifetime and more stable transmission than the table-driven or on-demand approaches. Simulation results reveal that the path lifetime of the LSR algorithm is longer than that of DSR, with a lower probability of path drop and avoiding rerouting. The LSR algorithm has a higher mean number of hoppings, because it always finds the most suitable path and is more stable than DSR, without searching again for a new path when the paths drop. [ABSTRACT FROM AUTHOR]

The K-coverage configuration that guarantees coverage of each location by at least K sensors is highly popular and is extensively used to monitor diversified applications in wireless sensor networks. Long network lifetime and high detection quality are the essentials of such K-covered sleep-scheduling algorithms. However, the existing sleep-scheduling algorithms either cause high cost or cannot preserve the detection quality effectively. In this paper, the Pre-Scheduling-based K-coverage Group Scheduling (PSKGS) and Self-Organized K-coverage Scheduling (SKS) algorithms are proposed to settle the problems in the existing sleep-scheduling algorithms. Simulation results show that our pre-scheduled-based KGS approach enhances the detection quality and network lifetime, whereas the self-organized-based SKS algorithm minimizes the computation and communication cost of the nodes and thereby is energy efficient. Besides, SKS outperforms PSKGS in terms of network lifetime and detection quality as it is self-organized. [ABSTRACT FROM AUTHOR]