In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method’s operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively.

Both coverage and connectivity are important problems in wireless sensor networks. As more and more non-orientation sensors are continuously added into the region of interest, the size of covered component and connected component increases; at some point, the network can achieve an entire coverage and full connectivity after which the network percolates. In this article, we analyze the critical density in non-orientation directional sensor network in which the orientations of the sensors are random and the sensors are deployed according to the Poisson point process. We propose an approach to compute the critical density in such a network. A collaborating path is proposed with the sum of field-of-view angles of two collaborating sensors being π. Then a correlated model of non-orientation directional sensing sectors for percolation is proposed to solve the coverage and connectivity problems together. The numerical simulations confirm that percolation occurs on the estimated critical densities. It is worth mentioning that the theoretical analysis and simulation results give insights into the design of directional sensor network in practice.

A directional sensor network is different from conventional wireless sensor networks. It uses directional sensors instead of omnidirectional ones in the network for different applications, and the effective sensing range is characterized by directionality and size-specific sensing angle. Therefore, conditions of directional sensor networks are dissimilar to those of generic wireless sensor networks for researches, especially on the sensing coverage. This study proposed a distributed approach to enhance the overall field coverage by utilizing mobile and direction-rotatable sensors in a directional sensor network. The algorithm makes sensors self-redeploy to the new location and new direction without global information by utilizing the features of geometrical Voronoi cells. Simulations were used to evaluate and prove the effectiveness of the proposed algorithm. The results show that the approach contributes to significant field coverage improvement in directional sensor networks.

Sensing coverage, which is one of vital issues in the design of wireless sensor networks (WSNs), can usually interact with other performance metrics such as network connectivity and energy consumption. Whatever the metrics, the fundamental problem is to know at least how many sensor nodes are needed to maintain both sensing coverage and network connectivity. In this paper, we propose a Percolation Model on Novel Gilbert Graph (PM-NGG) to obtain the critical density at which the network can become fully covered and connected considering the similarity between the occurrence of percolation and the formation of a covered and connected network. The PM-NGG is based on directional sensor network where sensors are assigned a determined sensing direction with angular intervals varying from 0 to 2 π. Furthermore, we define the sensing and communication model in directional sensor network in presence of channel randomness including deterministic path attenuation, shadow fading, and multipath fading. Besides, we discuss the coverage and connectivity together as a whole under the proposed model. It is worth mentioning that the theoretical analysis and simulation results of the relationship between critical density and transmitting power give insights into the design of directional sensor network in practice.

The popularity of Directional Sensor Network (DSN) is increasing due to their improved transmission range, spectral reusability, interference mitigation, and energy efficiency. In this paper, the radio module of the DSN is implemented using an eight-sector antenna array. Two types of sectored antennas, namely the Rectangular Patch Sectored Antenna (RPSA) and the Triangular Patch Sectored Antenna (TPSA), are proposed to operate at frequency of 2.4 GHz ISM band. The RPSA has a half-power beamwidth (HPBW) of 45° and a peak gain of 5.2 dBi, while the TPSA has an HPBW of 48° and a peak gain of 4.16 dBi. The design and performance evaluation of RPSA and TPSA in terms of gain, reflection characteristics (|S11|), and HPBW are conducted using Ansys High Frequency Structure Simulator (HFSS) and Vector Network Analyzer (VNA). To demonstrate the concept, the fabricated sectored antennas are connected to MicaZ Wireless Sensor Network (WSN) nodes using an indigenously designed Single Pole 8 Throw (SP8T) Radio Frequency (RF) switchboard. The performance of the DSNs based on RPSA and TPSA is evaluated using the Cooja simulator and a testbed consisting of MicaZ nodes. The results show that RPSA outperforms TPSA and omnidirectional-based WSNs in terms of power consumption, received signal strength, and packet delivery ratio.

This paper addresses two versions of a lifetime maximization problem for target coverage with wireless directional sensor networks. The sensors used in these networks have a maximum sensing range and a limited sensing angle. In the first problem version, predefined sensing directions are assumed to be given, whereas sensing directions can be freely devised in the second problem version. In that case, a polynomial-time algorithm is provided for building sensing directions that allow to maximize the network lifetime. A column generation algorithm is proposed for both problem versions, the subproblem being addressed with a hybrid approach based on a genetic algorithm, and an integer linear programming formulation. Numerical results show that addressing the second problem version allows for significant improvements in terms of network lifetime while the computational effort is comparable for both problem versions.

The wireless sensor networks (WSNs) come into prominence because they have the capability of changing our economy and life. A very vast range of applications supported by WSNs has served the human society very effectively; and, monitoring has been proven the most attention drawing application among these. As a tributary of sensor networks, Directional sensor networks (DSNs) consist of several camera sensors where nodes cover only a fraction of surrounding defined by a specific angle called vertex angle at a time. As sensors are randomly deployed, their sensing direction may overlap with one another and hence coverage optimization has become the most popular area for research over the years. In this paper , the problem of coverage is surveyed on the basis of the solutions proposed, and hence, classified into three categories as (1) Target coverage (2) Maximal coverage (subset of area coverage ) and (3) Maximum coverage with minimum sensors (subset of target coverage). The impact of various parameters of directional sensor networks as: sensor’s location, its working direction, number of orientations, FoVs on the performance of the deployed sensor network is analyzed. Maximum coverage with minimum sensors (subset of target coverage)

Recently, directional sensor networks that are composed of a large number of directional sensors have attracted a great deal of attention. The main issues associated with the directional sensors are limited battery power and restricted sensing angle. Therefore, monitoring all the targets in a given area and, at the same time, maximizing the network lifetime has remained a challenge. As sensors are often densely deployed, a promising approach to conserve the energy of directional sensors is developing efficient scheduling algorithms. These algorithms partition the sensor directions into multiple cover sets each of which is able to monitor all the targets. The problem of constructing the maximum number of cover sets has been modeled as the multiple directional cover sets (MDCS), which has been proved to be an NP-complete problem. In this study, we design two new scheduling algorithms, a greedy-based algorithm and a learning automata (LA)-based algorithm, in order to solve the MDCS problem. In order to evaluate the performance of the proposed algorithms, several experiments were conducted. The obtained results demonstrated the efficiency of both algorithms in terms of extending the network lifetime. Simulation results also revealed that the LA-based algorithm was more successful compared to the greedy-based one in terms of prolonging network lifetime.

Coverage is a crucial issue in directional sensor networks (DSNs), and a high coverage ratio ensures a good quality of service (QoS). However, a DSN encounters various problems because they use directional sensor nodes, which are characterized by directionality and a definite sensing angle. To address the area coverage problem of DSNs, this paper proposes a new sensing direction rotation approach to optimize coverage. First, we conduct grid partitioning in the target area and propose a coverage verification algorithm to justify the coverage situation of the grid points. Then, we utilize particle swarm optimization (PSO) to find an optimal sensing direction group of the directional sensor nodes to maximize the coverage ratio. Extensive simulation experiments were conducted to prove the effectiveness and reliability of our proposed approach. The results show that the approach improves the area coverage ratio of DSNs in various scenarios.

The barrier coverage of a wireless sensor network is an important surveillance application of Internet of Things. Barrier coverage guarantees that all intruders traversing the protected region are detected by a chain of connected sensors. However, when the sensors are randomly deployed, barrier gaps may occur due to deployment randomness or insufficient sensors. How to locate the barrier gaps and mend them is an important aspect in the network. In this paper, we study the barrier gap problem in weak barrier coverage and strong barrier coverage that consist of directional sensors, and the sensors are deployed by a line-based deployment strategy. A gap-finding algorithm is proposed to find subbarriers and barrier gaps. Two gap-mending algorithms are devised to mend barrier gaps in the network: One algorithm is a simple rotation algorithm that only rotates two critical sensors in two subbarriers to fix the gap, and the other algorithm is a chain-reaction rotation algorithm that rotates sensors in the subbarrier in a chain-reaction manner to mend the gap. We conduct extensive simulations to evaluate the performance of the proposed algorithms. Simulation results show that the proposed gap-mending algorithms can effectively fix barrier gaps and improve the probability of barrier success construction.