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Abstract Mobile wireless sensor network (MWSN) adapts to the requirements of mobility of civil and military network devices and has received a lot of attention. Designing an efficient, low‐energy, and fault‐tolerant routing protocol for the frequently changing topology of highly mobile MWSNs is challenging. Most routing protocols today only support the movement of sensor nodes or only sink. This paper proposes a Multiple Mobile Sinks Coverage Maximization based hierarchical routing protocol for mobile wireless sensor networks (MMSCM), which allows sensor nodes and sinks to move simultaneously. The MMSCM divides the monitoring area into virtual grids and deploys a movable sink in each grid cell. Sink selects the appropriate next hop for each cluster head in the area to build an optimal routing forest and collects data from all routing tree root nodes at a uniform speed along the specified path with maximum coverage. Simulation results show that, compared with current advanced routing algorithms, the MMSCM protocol can better solve the network delay problem and improve data throughput while extending the network life cycle, which is more suitable for time‐sensitive application scenarios.

Mobile wireless sensor network (MWSN) adapts to the requirements of mobility of civil and military network devices and has received a lot of attention. Designing an efficient, low‐energy, and fault‐tolerant routing protocol for the frequently changing topology of highly mobile MWSNs is challenging. Most routing protocols today only support the movement of sensor nodes or only sink. This paper proposes a Multiple Mobile Sinks Coverage Maximization based hierarchical routing protocol for mobile wireless sensor networks (MMSCM), which allows sensor nodes and sinks to move simultaneously. The MMSCM divides the monitoring area into virtual grids and deploys a movable sink in each grid cell. Sink selects the appropriate next hop for each cluster head in the area to build an optimal routing forest and collects data from all routing tree root nodes at a uniform speed along the specified path with maximum coverage. Simulation results show that, compared with current advanced routing algorithms, the MMSCM protocol can better solve the network delay problem and improve data throughput while extending the network life cycle, which is more suitable for time‐sensitive application scenarios. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing has emerged as a promising paradigm where location-based sensing tasks are outsourced to mobile participants carrying sensor-equipped devices. A critical issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participants, which requires participants’ precise locations and thus raises privacy concerns. In this paper, we are motivated to develop a privacy preserving participant recruitment scheme for mobile crowdsensing, which maximizes the spatial coverage of the sensing range while protecting participants’ location privacy against an untrusted crowdsensing platform. Briefly, we propose a utility-assured location obfuscation mechanism operated in a hexagonal grid system, which the participants can follow to locally perturb their locations with personalized privacy demands. Given the obfuscated locations, we efficiently solve a coverage-maximized participant recruitment problem with the budget constraint by using a deterministic rounding algorithm. Considering the existence of biased sensing data incurred by location obfuscation, we further develop a fault-aware crowdsensing framework to improve the robustness of the recruitment strategy, where a constant-approximation algorithm is applied to select participants against any number of unqualified sensing results. Extensive simulations on real-world location datasets and Uber’s geospatial indexing system validate the efficacy of our location obfuscation mechanism and participant recruitment schemes in mobile crowdsensing systems. [ABSTRACT FROM AUTHOR]

The Wireless Sensor Network (WSN) plays a significant function in today's environment. In general, WSN is widely used in IoT automation. The WSN's sensors will communicate with one another to share the information they've gathered. One of the most common challenges in Wireless Sensor Networks is the issue of coverage. A number of algorithms have been developed to address the coverage issue. We look at a range of coverage maximisation tactics in this survey. Coverage maximisation strategies are divided into groups based on their effectiveness. The merits and downsides of the algorithms are assessed in light of the surrounding circumstances. [ABSTRACT FROM AUTHOR]

This paper develops an effective approach for the 3D deployment of a heterogeneous set of unmanned aerial vehicles (UAVs) acting as aerial base stations that provide maximum wireless coverage for ground users in a given geographical area. This problem is addressed in two steps. First, in order to maximize the utilization of each UAV, its optimal flight altitude is found based on the UAV's transmit power which provides maximum coverage radius on the ground. The UAVs are classified into separate groups based on their transmit powers and optimal flight altitudes. Next, given a repository of UAVs belonging to different classes, the proposed technique finds an optimal subset of the available UAVs along with their optimal 3D placement to provide the maximum network coverage for a given area on the ground with the minimum power consumption. This optimization problem is proved to be NP-hard, for which a novel algorithm is proposed to solve the problem. Simulation results demonstrate the effectiveness of the proposed solution and provide valuable insights into the performance of the Heterogeneous UAV-supported small cell networks.

With the occurrence of a disaster, the conventional cellular network becomes non-functional. To provide connectivity to the affected users in such a scenario, we propose a novel multi-hop device-to-device (D2D) communication framework to connect to an active base station (BS). The goal of the proposed work is to maximize the number of covered users in the disaster-affected area within a given time frame. Joint routing and scheduling is imperative in a multi-hop network; however, the existing works on joint routing and scheduling optimization consider that the source–destination (user–BS) pairs are known beforehand or fixed. This is an inefficient approach when maximizing the number of covered users in a time-bounded communication set-up. Consequently, we propose a novel multi-hop D2D framework with joint source–destination pairing, routing and scheduling optimization. The optimization problem is formulated as an integer linear programming (ILP) problem. Further, due to the high time complexity of ILP, a low complexity graph-based scheduling constraint aware routing and pairing algorithm is proposed, resulting in a significant reduction in processing time compared to the optimal solution. The proposed algorithm also outperforms shortest path routing based scheduling in terms of users covered in the disaster-affected area. [ABSTRACT FROM AUTHOR]

The future wireless networks promise to provide ubiquitous connectivity to a multitude of devices with diversified traffic patterns wherever and whenever needed. For the sake of boosting resilience against faults, natural disasters, and unexpected traffic, the unmanned aerial vehicle (UAV)-assisted wireless communication systems can provide a unique opportunity to cater for such demands in a timely fashion without relying on the overly engineered cellular network. However, for UAV-assisted communication, issues of capacity, coverage, and energy efficiency are considered of paramount importance. The case of non-orthogonal multiple access (NOMA) is investigated for aerial base station (BS). NOMA's viability is established by formulating the sum-rate problem constituting a function of power allocation and UAV altitude. The optimization problem is constrained to meet individual user-rates arisen by orthogonal multiple access (OMA) bringing it at par with NOMA. The relationship between energy efficiency and altitude of a UAV inspires the solution to the aforementioned problem considering two cases, namely, altitude fixed NOMA and altitude optimized NOMA. The latter allows exploiting the extra degrees of freedom of UAV-BS mobility to enhance the spectral efficiency and the energy efficiency. Hence, it saves joules in the operational cost of the UAV. Finally, a constrained coverage expansion methodology, facilitated by NOMA user rate gain is also proposed. Results are presented for various environment settings to conclude NOMA manifesting better performance in terms of sum-rate, coverage, and energy efficiency.

This paper presents a molecular force-based deployment algorithm of charging stations according to the principle of intermolecular forces in physics to expand the flight coverage of electric-powered multi-rotor Unmanned Aerial Vehicles (UAV). With the help of this algorithm, a multi-rotor UAV can reach anywhere in the specific area by charging at the charging station several times. In this algorithm, a number of equal circles are used to cover the specific area (in a two-dimensional plane), and the center of each circle denotes a charging station. The radius of these circles is equal to the radius of action of the UAV. The number of the circles is set by the users. Under the combined effect of three virtual forces, the centers of the circles, called nodes, keep moving within the specific area, and multiple iterations are performed to adjust the location of each node. Finally, a proper deployment scheme for the charging stations is generated, which can achieve the working area maximization of the UAV by a certain number of charging stations. Simulation experiments were executed, and the results under different conditions show that the proposed algorithm can meet the expected requirements and has an advantage over three other algorithms in terms of coverage ratio. The experiment results also indicate that in the case of dense node density, the proposed algorithm has a better coverage performance than the case of sparse node density. The experimental data are available at https://figshare.com/projects/MFA/24064. The codes will be published later. [ABSTRACT FROM AUTHOR]

The internet of Things (IoT) has attracted significant attention in many applications in both academic and industrial areas. In IoT, each object can have the capabilities of sensing, identifying, networking and processing to communicate with ubiquitous objects and services. Often this paradigm (IoT) using Wireless Sensor Networks must cover large area of interest (AoI) with huge number of devices. As these devices might be battery powered and randomly deployed, their long-term availability and connectivity for area coverage is very important, in particular in harsh environments. Moreover, a poor distribution of devices may lead to coverage holes and degradation to the quality of service. In this paper, we propose an approach for self-organization and coverage maximization. We present a distributed algorithm for “Maintaining Connectivity and Coverage Maximization” called M C C M . The algorithm operates on different movable devices in homogeneous and heterogeneous distribution. It does not require high computational complexity. The main goal is to keep the movement of devices as minimal as possible to save energy. Another goal is to reduce the overlapping areas covered by different devices to increase the coverage while maintaining connectivity. Simulation results show that the proposed algorithm can achieve higher coverage and lower nodes’ movement over existing algorithms in the state of the art.

Spatial crowdsourcing and crowdsensing are two emerging crowdsourcing paradigms, which enable a variety of location-based query and sensing tasks. In spatial crowdsourcing, mobile workers are required to travel physically to target locations in order to complete query tasks. Most existing work, hence, has focused on designing efficient query task assignment schemes to maximize the task completion rate under traveling constraints of workers for spatial crowdsourcing systems. In crowdsensing, on the other hand, sensor recordings of workers’ smartphones are of interest and have been collected to build various applications. Therefore, work concerning crowdsensing has strived to maximize the coverage area of sensor trajectories by selecting a set of workers. In this paper, we investigate the integration of these two paradigms. We notice a key link between these paradigms: While a worker is traveling to the target location of a query task, his trajectory may provide valuable coverage for a sensing task. Therefore, we propose a task management framework, named SpatialRecruiter, to efficiently match workers to the merged query and sensing tasks. We propose two coverage estimation functions to compute the coverage potential of a worker. Then, we design a greedy heuristic to select and assign workers. The experimental results on a real-world dataset demonstrate that the proposed strategies are efficient and effective in meeting the requirements of both paradigms. [ABSTRACT FROM AUTHOR]

• This research will be helpful for researchers who want to use wireless sensor networks with increased network coverage in their applications. • Enhances network coverage with optimized energy. • Achieve maximum coverage but no increase in sensor nodes means no increase in the cost of equipment. • Provide comparative analysis with other existing algorithms. The distribution of sensor nodes is the key factor in mobile wireless sensor networks (WSNs) to enhance the functionality of the network. The effectiveness of a wireless sensor network can be increased by the efficient distribution of several sensor nodes. Previously researchers considered existing algorithms or their hybrid for the effective distribution of sensor nodes. Those only optimize energy or enhance network coverage none of them considered both aspects with the minimum cost of equipment. This piece of work proposes a novel bio-inspired ruminant algorithm. The algorithm is developed as a result of the in- spiration of the efficient digestive system of ruminant animals. That takes a large amount of raw food as input and produces an optimal value of food that is full of energy. The proposed algorithm is validated on several benchmark functions. By keeping inspiration in mind, the proposed algorithm not only applied to enhance network coverage of WSNs with optimized energy and sensor nodes distribution, which increases the lifetime of the equipment. Furthermore, the results of the algorithm show that if network coverage enhances more optimized value of energy is achieved with no increase in the number of deployed sensors. That proves the no increase in the cost of the equipment with more network coverage on enhanced lifetime. [ABSTRACT FROM AUTHOR]

The performance of the wireless sensor networks, composed of static sensor nodes, is significantly influenced by the random deployment. Often, the sensors are scattered incorrectly and hence positioned inaccurately. A frequent criticism of the random deployment of stationary nodes is that it might cause coverage holes in the field being monitored. In this paper, we propose adding mobile sensor nodes after the initial deployment of the stationary nodes to overcome the coverage holes problem. To achieve optimal coverage, we first described the network dynamics using Markov chain model, and then harmony algorithm is employed to find the optimal solution for the added mobile nodes intended for covering of the holes. The proposed algorithm determines the best locations of the mobile nodes that need to be added after the initial deployment of the stationary nodes. The performance of the proposed algorithm was evaluated using several metrics, and the simulation results demonstrated that, compared to similar state-of-the-art algorithms, the proposed algorithm can optimize the network coverage in terms of the overall coverage ratio, coverage degree and the number of additional mobile nodes. [ABSTRACT FROM AUTHOR]

Visual sensors can be used to retrieve still images or video streams in a large set of monitoring applications, providing valuable information of the monitored environment. In fact, camera-enabled sensors are directional and the viewed area depends on their orientations. As the positions and orientations of the embedded cameras may not be optimal after deployment, the area viewed by sensors may be adjusted using some optimization algorithm. Although the problem of coverage optimization has been addressed by some research works, coverage maximization arises as a challenging problem where the number of camera views over a set of targets has to be maximized for higher availability and increased number of viewed perspectives of targets. This paper proposes centralized algorithms to compute orientations of rotatable cameras to achieve maximized coverage in wireless visual sensor networks, bringing significant results to this research area. [ABSTRACT FROM PUBLISHER]

Self-deployment of sensors with maximized coverage in Underwater Acoustic Sensor Networks (UWASNs) is challenging due to difficulty of access to 3-D underwater environments. The problem is further compounded if the connectivity of the final network is desired. One possible approach to this problem is to drop the sensors on the water surface and then move them to certain depths in the water to maximize the 3-D coverage while maintaining the initial connectivity. In this paper, we propose a fully distributed node deployment scheme for UWASNs which only requires random dropping of sensors on the water surface. The idea is based on determining the connected dominating set (CDS) of the initial network on the surface and then adjust the depths of all neighbors of a particular dominator node (i.e., the backbone of the network) for minimizing the coverage overlaps among them while still keeping the connectivity with the dominator. The process starts with a leader node and spans all the dominators in the network for repositioning them. In addition to depth adjustment, we studied the effects of possible topology alterations due to water mobility caused by several factors such as waves, winds, currents, vortices or random surface effects, on network coverage and connectivity performance. On the one hand the mobility of nodes may help the topology to get stretched in 2-D, which helps to maximize the coverage in 3-D. On the other hand the mobility may cause the network to get partitioned where some of the nodes are disconnected from the rest of the topology. We investigated the best node deployment time where 2-D coverage is maximized and the network is still connected. To simulate the mobility of the sensors, we implemented meandering current mobility model which is one of the existing mobility models for UWASNs that fits our needs. The performance of the proposed approach is validated through simulation. Simulations results indicate that connectivity can be guaranteed regardless of the transmission and sensing range ratio with a coverage very close to a coverage-aware deployment approach. [ABSTRACT FROM AUTHOR]

This paper takes into consideration the problems related to monitoring a phenomenon of interest in an unknown and open environment using multiple mobile sensor (MS) nodes. We propose an environment learning-based phenomenon monitoring system that iteratively learns about the environment and relocates MS nodes to optimal positions, where MS nodes can attain a high weighted sensing coverage and maintain network connectivity. In this paper, finding optimal positions for MS nodes is defined as the connectivity-constrained coverage maximization problem. An integer linear programming optimization formulation is proposed to find the solution. We also propose three heuristics algorithms to efficiently solve the connectivity-constrained coverage maximization problem. Simulation results show that the proposed algorithms outperform other approaches in terms of the weighted coverage efficiency and energy efficiency. [ABSTRACT FROM PUBLISHER]

In this paper, we study mobile sensor network (MSN) architectures and algorithms for monitoring a moving phenomenon in an unknown and open area using a group of autonomous mobile sensor (MS) nodes. Monitoring a moving phenomenon involves challenges due to limited communication/sensing ranges of MS nodes, the phenomenon’s unpredictable changes in distribution and position, and the lack of information on the sensing area. To address the challenges and meet the objective of the maximization of weighted sensing coverage, we propose a novel scheme, namely VirFID (Virtual Force (VF)-based Interest-Driven moving phenomenon monitoring). In VirFID, MS nodes move toward the positions where more interesting sensing data can be obtained by utilizing the virtual force, which is calculated based on the distance between MS nodes and sensed values in the area of interest. MS nodes also perform network-wise information sharing to increase the weighted sensing coverage. Depending on the level of information used, three variants of VirFID are evaluated: VirFID-LIB (Local Information-Based), VirFID-GHL (Global Highest and Lowest), and VirFID-IBN (Interests at Boundary Nodes). In addition, an analytical model for estimating MSN speed is designed. Simulations are performed to compare the performance of three VirFID variants with other approaches. Our simulation results show that VirFID algorithms outperform other schemes in terms of the weighted coverage efficiency, and VirFID-IBN achieves the highest weighted coverage efficiency among VirFID variants. [ABSTRACT FROM AUTHOR]

Deployment of sensor nodes is one of the crucial factors in mobile wireless sensor networks for improving the performance of the network. The network's lifetime primarily depends on the consumed energy and area coverage by the sensor nodes. The efficiency of mobile wireless sensor networks increases by the efficient deployment of the sensors. Coverage and energy consumption mainly depends on the effective deployment schemes of sensors. This article presents an energy-efficient coverage optimization technique with the help of the Voronoi-Glowworm Swarm Optimization-K-means algorithm. In this approach, Glowworm Swarm Optimization, K-means algorithm, and Voronoi cell structure enhance the coverage area with a minimum number of active nodes. This approach considers optimum sensing radius calculation for efficient sensor deployment. Furthermore, the proposed method improves the lifetime of the deployed network by decreasing the consumed energy by the deployed sensor nodes using multi-hop transmission and the sleep-wake mechanism. The simulation result shows that area coverage is achieved by the proposed method up to 99.99% with the optimum number of active sensor nodes. [ABSTRACT FROM AUTHOR]

In the mobile wireless sensor network (MWSN) field, there exists an important problem—how can we quickly form an MWSN to cover a designated working area on the ground using an unmanned aerial vehicle (UAV) swarm? This problem is of significance in many military and civilian applications. In this paper, inspired by intermolecular forces, a novel molecular force field-based optimal deployment algorithm for a UAV swarm is proposed to solve this problem. A multi-rotor UAV swarm is used to carry sensors and quickly build an MWSN in a designated working area. The necessary minimum number of UAVs is determined according to the principle that the coverage area of any three UAVs has the smallest overlap. Based on the geometric properties of a convex polygon, two initialization methods are proposed to make the initial deployment more uniform, following which, the positions of all UAVs are subsequently optimized by the proposed molecular force field-based deployment algorithm. Simulation experiment results show that the proposed algorithm, when compared with three existing algorithms, can obtain the maximum coverage ratio for the designated working area thanks to the proposed initialization methods. The probability of falling into a local optimum and the computational complexity are reduced, while the convergence rate is improved. [ABSTRACT FROM AUTHOR]