Good deployment of sensors empowers the network with effective monitoring ability. Different from omnidirectional sensors, the coverage region of a directional sensor is determined by not only the sensing radius (distance), but also its sensing orientation and spread angle. Heterogeneous sensing distances and spread angles are likely to exist among directional sensors, to which we refer as heterogeneous directional sensors. In this paper, we target on a bounded monitoring area and deal with heterogeneous directional sensors equipped with locomotion and rotation facilities to enable the sensors self-deployment. Two Enhanced Deployment Algorithms, EDA-I and EDA-II, are proposed to achieve high sensing coverage ratio in the monitored field. EDA-I leverages the concept of virtual forces (for sensors movements) and virtual boundary torques (for sensors rotations), whereas EDA-II combines Voronoi diagram directed movements and boundary torques guided rotations. EDA-I computations can be centralized or distributed that differ in required energy and execution time, whereas EDA-II only allows centralized calculations. Our EDA-II outperforms EDA-I in centralized operations, while EDA-I can be adapted into a distributed deployment algorithm without requiring global information and still achieves comparably good coverage performance to its centralized version. To the best of our knowledge, this is perhaps the first work to employ movements followed by rotations for sensors self-deployment. Performance results demonstrate that our enhanced deployment mechanisms are capable of providing desirable surveillance level, while consuming moderate moving and rotating energy under reasonable execution time.

With the growing popularity of wireless mobile sensors, automated sensors deployment in a smart sensing environment has become practical and feasible. Once the deployment algorithm determines moving destinations (goals) for all sensors, however, the problem of how to schedule collision-free moving paths to reach the goals safely remains largely unaddressed in the wireless sensor networking (WSN) literature. In this paper, we propose a collision-free path planning (CFPP) mechanism, based on geometric formulations and batched movements, to address the sensors deployment problem. Our proposed CFPP mechanism ensures 100% sensors goal reachability, which is critical for most WSN monitoring applications that require sufficient sensing coverage to operate correctly. Performance results show that our CFPP outperforms other existing path-planning mechanisms in terms of computation latency, energy consumption, and sensors reachability (goals reaching success probability).

One critical issue, for a wireless sensor network (WSN) to operate successfully, is to provide sufficient sensing coverage. Define the smart sensing environment as a sensing system with the capability to sense the environment and respond properly in an automated manner. In this paper, we target on smart sensing environments and deal with heterogeneous sensors (here sensor heterogeneity is defined as sensors having different sensing ranges) equipped with actuation facilities to assist in the sensor self-deployment. A coverage-aware sensor automation (CASA) protocol is proposed to realize an automated smart monitoring network. Two centralized algorithms are included in the CASA protocol suite: enhanced virtual forces algorithm with boundary forces (EVFA-B) and sensor self-organizing algorithm (SSOA). Unlike most previous works that tackle the deployment problem only partially, we intend to address the problem from both global deployment (EVFA-B) and local repairing (SSOA) perspectives. The EVFA-B protocol exerts weighted attractive and repulsive forces on each sensor based on predefined distance thresholds. Resultant forces then guide the sensors to their suitable positions with the objective of enhancing the sensing coverage (after a possibly random placement of sensors). Furthermore, in the presence of sensor energy depletions and/or unexpected failures, our SSOA algorithm is activated to perform local repair by repositioning sensors around the sensing void (uncovered area). This capability of local recovery is advantageous in terms of saving the communication and moving energies. Performance of the proposed sensor deployment strategies is evaluated in terms of surveillance coverage, monitoring density, network self-healing competence, and moving energy consumption. We also implement our CASA protocol suite in a real-life monitoring network (MoNet) to demonstrate the protocol feasibility and validate the MoNet detection capability of emergency events.