Direction sensor networks are robust systems employed for detecting phenomena in environments or monitoring objects therein. They have a wide range of applications across many different industries and fields. In terms of the availability of resources, direction sensor networks deal with two problems: over-provision and under-provision of sensors. Over-provision occurs when there are too many sensors in the monitoring area, resulting in wasted resources and unnecessary energy consumption as some sensors are not well utilized. In contrast, under-provision occurs when there are too few sensors in the monitoring area, leading to the coverage of targets not satisfied. To ensure balanced coverage in under-provisioned environments, sensors must be placed so as to provide nearly equal fault tolerance to all objects, thereby enhancing the operational efficiency of the network. On the other hand, in over-provisioned environments, the number of active sensors needs to be minimized so that energy consumption is efficient. This study focuses on solving the Q-coverage problem in adjustable-orientation direction sensor networks, aiming to optimize a bi-level objective: maximizing network coverage balancing while minimizing sensor count in both under-provisioned and over-provisioned environments. The proposed Improved Genetic Algorithm utilizes novel operators, including Greedily-tuned Simulated Binary Crossover and Adaptive Polynomial Mutation. Evaluation parameters, including the Q-Balancing Index, Distance Index, Coverage Quality, Power Consumption, and the number of active sensors, demonstrate the efficiency of the proposed algorithm compared to other existing methods.

Connectivity is one of the most challenging issues in Wireless Sensor Network (WSN). Connectivity problems in WSN seek to guarantee a satisfactory communication capability where all mobile sensors can connect to a base station via relay nodes in all data gathering events. In this paper, we focus on minimizing the number of relay nodes while ensuring connectivity in Mobile Wireless Sensor Networks. We propose an improved heuristic algorithm named Clustered Steiner Tree Heuristic (CSTH) to solve this problem in two phases. The first phase is Node Anchoring, which utilizes a greedy approach to find anchor points among clusters of mobile sensors. The second phase is called Steiner Relay Placement, in which a Steiner tree-based heuristic is used to minimize the number of relay nodes while maintaining connectivity in each cluster. Experiments were performed to compare CSTH with previous state-of-the-art heuristics for the problem. Results show that our algorithm can significantly improve the number of required relay nodes as well as computation time.

Target coverage, connectivity, and fault tolerance are three challenging issues in wireless sensor networks. Target coverage aims to provide a sufficient monitoring quality where all targets in the surveillance region are covered by sensor nodes. Meanwhile, connectivity and fault tolerance seeks to guarantee a satisfactory communication capability where all sensors can connect to base station via relay nodes, while always able to find a backup path in case of failure. In this paper, we focus on minimizing the number of nodes (i.e., sensor nodes and relay nodes) while ensuring target coverage, connectivity and fault tolerance in wireless sensor networks. We approach this problem as two sub-problems. The first is Target Coverage, which requires placing sensor nodes to cover all targets. The second is Network Connectivity and Fault Tolerance, in which relay nodes need to be placed to connect sensor nodes to the base station, along with a backup path in case of failure. We propose an improved formulation of the Fault Tolerance constraint, as well as a new heuristic algorithm, MUTSP, which solves the first phase using a greedy approach, and the second phase with a spanning tree formulation. This method is compared and measured against previous state-of-the-art heuristics for the problem in our experiments. The results show that our algorithm can significantly improve the number of required nodes as well as computation time.