Connectivity is an important aspect of wireless sensor networks. Providing connectivity in 3D wireless sensor networks is very crucial when the network size is larger. There are several studies that discuss connectivity in the 2D sensor network. However, these are not always suitable when the network area is non-planar. In this paper, we focus on the connectivity of sensor networks in 3D space. We propose a Delaunay tetrahedron-based relay node placement strategy for connectivity and evaluate sensor density for the requirement. Sensor networks on the mountain are a form of a 3D surface topography. The target region is triangulated using the Delaunay triangle. Reduction in hops for data forwarding consumes less energy. Using such techniques, we developed an energy-efficient algorithm. Through simulation, we demonstrate that the proposed method guarantees connectivity with an optimal number of sensor nodes.

Wireless sensor networks (WSNs) are now frequently used to collect all necessary sensory data for decision-making. As a result, it is common for the sensor and sink nodes to synchronize their messages quickly. Due to this consequence, the sensor node consumes much energy because of the enhanced network traffic. Conventional sensor node batteries often have a small number of charge cycles, and recharging the battery at an inaccessible location is impracticable. Some practical concerns are feasible, such as network life, coverage/connectivity, which drains the nodes’ batteries without frequent maintenance, and network performance. To overcome the problem of an energy shortage, energy harvesting (EH) can offer a limitless supply of energy resources for networks. Solar, wind, mechanical, and thermal are all possibilities. Related to the intra-networks-based solution, we have designed an effective solution to solve the above issues called the constrained relay harvesting node placement (CRHNP) approach in EH-WSNs. This algorithm worked based on efficient coverage awareness and showed effective geometric-based coordination among nodes. Finding relay harvesting nodes and employing multiple maximum covering sets scheduling approaches in EH-WSNs are some of the functionalities supported by the CRHNP method. The proposed work’s results are classified into five categories: network lifetime, coverage degree, optimum RH node placement, node performance, and network sustainability performance assessment. The performance of the proposed method outperforms all other algorithms in each sector.

Solar energy harvesting (EH) is one of the best promising approaches toward perpetual network operation, and it is implemented in various regions of interest (RoIs). However, saving external energy is the essential prime factor in any embedded sensor with finite storage capacity. Generally, the energy conversion rate of the solar system is too fast due to various environmental conditions. Besides, ambient resource energy is noncontrollable, and rechargeable battery only operates in outdoor ecological systems. Frequent environmental fluctuation in their prediction is imperative for initial energy control. Considering the challenges mentioned above, in this article, we propose a modified PROfile energy (Pro-energy) prediction technique to control unnecessary errors in solar-based harvesting systems related to the sensing devices, which estimates the most similar profile-based energy observation in previous time slots. Our proposed method uses prior energy measurements to show future energy status in the respective time slots. Experimental observations on various performance matrices validate that the modified Pro-energy prediction technique exhibits more promising and superior performance than existing EMWA, weather-conditioned moving average (WCMA), and Pro-energy methods.

Wireless Sensor Networks (WSNs) combine low power energy-constrained sensor nodes. Presently, the application of sensor networks has been increased in many prominent places, namely green smart city, environment system monitoring, under coal mines surveillance, Internet of Things (IoT) and health monitoring, etc. One of the challenging issues of the sensor network is the energy scarcity problem because the sensor node is deployed in an unreachable area. The charging capacity of a traditional node battery is too much short. However, replacing or recharging the sensor node recharge battery is not practical; it may create dis-connectivity in the network. Thus, one solution has been found to recharge sensor node battery that is called the energy harvesting technique. It is present in the environment in different ways such as solar, wind, mechanical, etc. Another issue is based on the coverage problem, which means each target should be covered at least one node for continuous monitoring at a particular location. Data would also be sent to the base station. Related to the above issue, we have proposed one technique called maximum target covering lifetime (MTCL) in EH-WSNs. However, the MTCL method provides a complete solution to overcome the target coverage problem. This method is entirely based on the sleep/wake schedule to increase the network's network lifetime. Work performance is stated to be art and beats other existing methods.

Connectivity is a critical prerequisite for the effective operation of data gathering and forwarding procedures in Wireless Sensor Networks (WSNs). Failure of several sensor nodes in the network makes the base station incapable of receiving data from all the portions of the target area. Multiple partitions that are unable to communicate with one another are formed. Such networks are repaired using additional mobile relays. These relays cooperatively work together to create links among the partitions. In order to complete their task, they require a quick, communication-efficient, and machine learning-based approach. Reinforcement learning has evolved as a very efficient approach with long-term solutions in solving such problems. In this work, we propose a Reinforcement Learning-based Connectivity Restoration (RLCR) method that applies an intelligent machine learning algorithm for collaborative movement and connection establishment among partitions using relay nodes. It takes into account the actions of other agents and is capable of learning complicated multi-agent coordinating strategies. In a subsequent step, relay selection and connectivity maintenance have also been included. In RLCR, relays search for isolated partitions while maintaining communication with one another. Besides, we use Python to simulate the procedure and compare the results to various current methodologies. The experimental analysis illustrates that the proposed RLCR method performs better than the standard algorithms.

In wireless sensor networks (WSNs), the sensor nodes collect data from the target area and send it to the base station through a multi-hop connection. As a result, high energy dissipation of the sensor node may occur. Some times failure of nodes on large scale create partitions in the network. Also, the presence of obstacles is the reason for the coverage gap in networks. The introduction of mobile nodes has been measured to be a worthy approach to significantly decrease the energy dissipation of the static sensor nodes. This job is realized by considering the route through which the mobile collectors move to collect data from the sensor nodes. Another challenging job is to find the path of the mobile collector in an area with obstacles. In this work, an obstacle aware connectivity establishment (OACE) using mobile nodes in wireless sensor networks is proposed. The mobile robot collects data from each partition and carries forward to other partitions so data finally reaches to sink. To achieve this, a Delaunay triangulation based obstacle avoidance clustering has been performed. The performance of the proposed algorithm has been shown by varying the sensor nodes to a single mobile node and multiple mobile nodes. The number of partitions in the network also plays an important role in network performance. Simulation results show that the proposed method with multiple mobile collectors is more energy-efficient with less latency as compared to the single mobile collector for data collection.

Network connectivity is the prime requirement in a wireless sensor network because the sensed data reaches to sink node. Failure of multiple nodes also affects the network area coverage. Deployment of sensor nodes in a federated network is a challenging problem. The recent development of small and low-price sensors has ensouled to solve such issues by deploying additional sensor nodes. Random deployment does not guarantee maximum coverage and network connectivity so, precise location can be followed by mobile devices that drop sensors in the field. The deployment also affects the network coverage. In this work, we propose a medial axis based approach that determines the pattern for node deployment. We focus on regaining network connectivity with optimal coverage. The number of nodes affects the network repairing cost, so we target to minimize this number. We simulated using python, and the result shows better performance.

Wireless sensor networks are formed with very small sensor devices with limited energy and short transmission range. Sensors are randomly deployed in remote areas with harsh conditions.Due to this, their distribution are non-uniform. In a hostile environment, the occurring of large scale failure is very often and it creates partitions of different sizes in the network. Restoring the network connectivity is very crucial for network functioning. Typically sensors are static and communication is bi-directional and we need to reconnect the network with the additional relay sensor nodes. After reconnecting the network, overhead on relays is very high and thus it consumes more energy and high computational power. Optimal number of relay nodes required for reconnecting the network is an NP-hard problem. Here, we proposed a new heuristic approach called adaptive Cluster-based Relay Placement Algorithm.Our approach is to reconnecting the networks based on finding the nearest partitions using their centroids. Using the proposed algorithm, relays are placed on the denser part of the partitions and number of required relays are also minimum. It reduces the network recovery time and increases the network lifetime. Through simulation, we show that the proposed algorithm performs better than the existing approaches.